[orcid=0000-0002-9079-0960] [orcid=0000-0001-9789-8483]
[orcid=0000-0001-6214-1077] [orcid=0000-0002-7401-5120]
[orcid=0000-0003-4445-3135] [orcid=0000-0001-6405-8402]
[orcid=0000-0001-6432-5187] [orcid=0000-0003-2759-0306]

1]Khalifa University Center for Autonomous Robotic Systems (KUCARS), Khalifa
University, United Arab Emirates 2] School of Aerospace Engineering, Beijing
Institute of Technology, China. [1]First two authors has equal contribution

# MuLA-GAN: Multi-Level Attention GAN for Enhanced Underwater Visibility

Ahsan B. Bakht  Zikai Jia  Muhayy ud Din  Waseem Akram  Lyes Saad Soud  Lakmal
Seneviratne  Defu Lin  Shaoming He  Irfan Hussain  [  [

###### Abstract

The underwater environment presents unique challenges, including color
distortions, reduced contrast, and blurriness, hindering accurate analysis. In
this work, we introduce MuLA-GAN, a novel approach that leverages the
synergistic power of Generative Adversarial Networks (GANs) and Multi-Level
Attention mechanisms for comprehensive underwater image enhancement. The
integration of Multi-Level Attention within the GAN architecture significantly
enhances the model’s capacity to learn discriminative features crucial for
precise image restoration. By selectively focusing on relevant spatial and
multi-level features, our model excels in capturing and preserving intricate
details in underwater imagery, essential for various applications. Extensive
qualitative and quantitative analyses on diverse datasets, including UIEB test
dataset, UIEB challenge dataset, U45, and UCCS dataset, highlight the superior
performance of MuLA-GAN compared to existing state-of-the-art methods.
Experimental evaluations on a specialized dataset tailored for bio-fouling and
aquaculture applications demonstrate the model’s robustness in challenging
environmental conditions. On the UIEB test dataset, MuLA-GAN achieves
exceptional PSNR $(25.59)$ and SSIM $(0.893)$ scores, surpassing Water-Net,
the second-best model, with scores of $24.36$ and $0.885$, respectively. This
work not only addresses a significant research gap in underwater image
enhancement but also underscores the pivotal role of Multi-Level Attention in
enhancing GANs, providing a novel and comprehensive framework for restoring
underwater image quality.

###### keywords:

Underwater Image Enhancement Generative Adversarial Networks (GANs)Spatio-
Channel Attention Computer Vision Real-time Image Processing

##  1 Introduction

In recent years, the vast potential of our oceans, from abundant resources to
environmental significance, has garnered increasing attention. The exploration
and exploitation of marine resources have become focal points for researchers.
However, the acquisition of clear and detailed underwater images faces
significant challenges, including blurriness, color distortion, and
degradation caused by light interference and complex underwater backgrounds,
or due to extreme weather conditions as observed by [1],[2]. These challenges
lead to the loss of crucial details, making visual tasks such as feature
extraction and target detection exceptionally demanding.

Addressing these challenges, the field of underwater image enhancement strives
to elevate the quality of underwater images, unlocking more effective
utilization of the visual information available [3]. This enhancement holds
significant practical applications, spanning target detection, marine
environment monitoring, wreck detection, and military operations. Applying
image processing techniques to improve these images can greatly benefit
various high-level computer vision tasks.

The quality of underwater images profoundly influences the efficacy of
underwater applications. Factors such as limited visibility, uneven lighting,
noise, and color distortion contribute to the difficulties in obtaining high-
quality underwater images [4]. Underwater image processing broadly falls into
two categories: image restoration and image enhancement [5]. While image
restoration aims to recover the original content of underwater images by
addressing issues like blurring and color distortion, image enhancement
techniques focus on improving visual appeal without necessarily adhering to a
physical model or aiming for an accurate restoration.

Figure 1: Proposed Dehazing Model Training Process: An input image transforms
within our generator network, integral to a GAN model. The resultant output
image undergoes rigorous evaluation by a discriminator network, in conjunction
with a reference image, for authenticity assessment. The generator refines its
output based on this evaluation, iteratively crafting more realistic images.
Its objective is to strategically deceive the discriminator while aligning
with the characteristics of the reference image, thereby mastering the nuances
of image enhancement.

Traditional underwater image enhancement methods often prioritize visually
pleasing results based on subjective criteria. These methods, simpler and
faster than model-based approaches, can produce appealing results but may
alter image features, impacting tasks such as object detection and
segmentation [6]. In contrast, model-based approaches leveraging deep learning
architectures have demonstrated superiority, particularly in preserving
features accurately. However, existing GAN-based approaches, including UGAN
[7], WaterGAN [8], and FunieGAN [9], while promising in image enhancement and
restoration, introduce noise in the generated images. Conventional
convolutional networks like UWCNN [10] and WaterNet [11] exhibit color
correction deviations, limiting their capabilities across diverse underwater
scenarios.

In response to the strengths and limitations of existing models, this paper
introduces MuLA-GAN, a novel GAN-based approach that integrates a spatio-
channel attention mechanism into the generator architecture. This integration
significantly enhances the model’s ability to perform effective image
enhancement and restoration. The primary contributions of this paper are:

  * •

Introducing MuLA-GAN, a novel and effective image enhancement method based on
a GANs model that incorporates a spatio-channel attention mechanism into the
generator architecture, significantly improving the model’s effectiveness.

  * •

Conducting an extensive qualitative and quantitative analysis on publicly
available datasets, including UIEB, U45, and UCCS. This analysis focuses on
the visual aspects of image enhancement and includes a detailed comparison of
the proposed method with state-of-the-art approaches across diverse scenes and
scenarios within these datasets.

  * •

Performing a comprehensive analysis on a distinct dataset collected in our
laboratory, primarily focused on bio-fouling and aquaculture applications.
This analysis provides insights into the model’s performance in diverse
environmental conditions.

##  2 Related Work

Image enhancement plays a crucial role in improving visual quality and
extracting meaningful information from images. Over the years, researchers
have explored various methodologies to enhance images, with two prominent
streams of investigation emerging. One branch primarily focuses on leveraging
traditional image processing techniques, while the other harnesses the power
of deep learning algorithms. In this section, we review some of the currently
available solutions making use of classical and deep learning-based
approaches.

###  2.1 Classical image enhancement techniques

The classical approaches to image enhancement through image processing often
involve manually designed techniques. For improving the overall visibility of
low-light images, contrast enhancement methods such as histogram equalization
[12], contrast-limited adaptive histogram equalization (CLAHE) [13], and gamma
correction are commonly utilized. These techniques aim to enhance the global
contrast and visibility of the images. The gray-world assumption and white
balancing are also widely used traditional color correction methods that are
commonly used to adjust the color and saturation of images. These fundamental
image-processing techniques have been traditionally utilized for image
enhancement and are now being increasingly adapted for underwater scenarios.
By applying these techniques to underwater images, researchers aim to improve
color accuracy and enhance the visual quality of underwater scenes.

Research studies have explored various image processing techniques for
improving the quality of underwater images. The contrast stretching and fuzzy
HE methods [14] have been used by researchers to enhance underwater image
quality. Additionally, the inversion of light absorption [15] has been
proposed as a solution to the underwater color correction problem. Fusion
enhancement strategies [16] have also been designed, involving the weighted
fusion of images after WB and HE. The dark channel prior (DCP) [17],
originally developed for dehazing, has found extensive use in underwater image
recovery. An underwater-specific variation of the DCP, known as the underwater
dark channel prior (UDCP) [18], utilizes the minima of green and blue channels
to compute dark channel images. The generalized dark channel prior (GDCP) [19]
incorporates adaptive color correction into image formation models for image
recovery. Submerged dark channel prior (SDCP) [20] has been proposed for
removing scattering and estimating the conduction equation in image
restoration. Real-time underwater application benchmarks have been established
by Perez et al. [21] for evaluating DCP-related models. The Jaffe-McGlamery
model [22],[23] is another commonly used underwater imaging model. A
physically accurate model based on the Jaffe-McGlamery model has been proposed
by Akkaynak et al. [24], [25], where the attenuation coefficient characterizes
target distance and reflection. Utilizing RGBD images, the Sea-thru method
[26] estimates backscattering and distance-dependent decay coefficients, which
play vital roles in enhancing underwater image quality. These image-processing
techniques aim to enhance contrast and improve the overall image quality of
underwater scenes. However, individual techniques are often developed based on
specific assumptions or for particular tasks, limiting their suitability for
addressing general underwater enhancement scenarios with significant
variability.

Figure 2: The proposed MuLA-GAN architecture, encompasses an encoder section
with convolutional layers, residual blocks, and attention blocks. The decoder
section employs straightforward transposed convolution layers. It also
provides insight into the internal structure of the residual block, tasked
with enhancing features and capturing intricate details.

###  2.2 Deep learning-based image enhancement techniques

Deep learning techniques have been successfully applied to underwater image
enhancement tasks, helping to improve the quality and visibility of images
captured in underwater environments. In this section, we briefly discuss a few
examples in this regard. For instance, Li et al. [8], developed WaterGAN deep
learning-based generative adversarial network model that performs network
training for reducing the color deviation in underwater images. Fabbri et al.
[7], developed UGAN by taking the idea of CycleGAN [27] that performs domain
transformation for the reconstruction of degraded images. Guo et al. [28]
developed a novel multi-scale dense GAN by adding a residual multi-scale dense
block to the generator resulting in improved enhancement performance. Islam et
al. [9] presented FUnIE-GAN by introducing a multi-model objective function in
the network that is specifically designed for online underwater image
enhancement. Similarly, Li et al. [10] worked on the synthesized underwater
image enhancement by developing UWCNN model. A gated fusion-based model is
also proposed by Li et al. [11] and named as Water-Net. Islam et al. [29]
introduced a novel Deep SESR model by adding dense residual-in-residual sub-
networks that provide both image enhancement and high-resolution underwater
images.

Qi et al. [30] proposed SGUIENet for image enhancement using semantic
information as input for region-wise feature learning in the enhancement
process. Such design allowed for better extraction of local features for
enhancement, resulting in a consistent and superior enhancement. Huang et al.
[31] proposed a novel adaptive group attention (AGA) used to select
complementary channels based on dependencies resulting in a reduction in the
number of parameters in attention. Furthermore, the proposed AGA is integrated
with Swin Transformer for an end-to-end underwater image enhancement network.
Cai et al. [32], proposed a CURE-Net that systematically enhances image
quality stepwise. The proposed model comprises three subnetworks, where the
initial two subnetworks employ attention mechanisms and gate fusion to acquire
comprehensive contextual features across multiple scales, while the third
subnetwork focuses on preserving intricate spatial details. Additionally, a
detailed enhancement block and a supervised recovery block were also
incorporated between these subnetworks to further enhance color accuracy and
refine fine details in the image restoration process.

##  3 Methodology

To achieve automated image enhancement, our focus lies in acquiring a mapping
G: X → Y. This mapping connects a source domain X, which encompasses distorted
images, to a desired domain Y, containing their enhanced counterparts. Our
chosen approach centers on adopting a conditional GAN-based model as shown in
Fig. 1 Within this model, the generator takes on the task of learning this
intricate mapping. This learning process unfolds through a dynamic interplay
with an adversarial discriminator, set as an iterative min-max game

###  3.1 Generator Architecture

Inspired by the principles of conditional GANs, we developed a deep neural
network architecture designed for automatic image enhancement tasks,
specifically tailored to improve image quality from a source domain (distorted
images) to a desired domain (enhanced images). As shown in Fig. 2. The
generator architecture leverages the foundation of a U-Net-based structure,
renowned for its effectiveness in image-to-image translation problems while
emphasizing efficiency for fast inference.

The network takes as input images with dimensions of 256x256 pixels and 3
color channels. It follows a fully convolutional design, with each layer
applying 2D convolutions using 4x4 filters. Batch Normalization and Leaky-ReLU
activation functions are used after each convolution layer to facilitate
network training and stability. The encoder consists of 5 blocks which
progressively reduce the spatial dimensions and learn features. It starts with
a convolutional layer with 32 output channels and strides of 2, followed by a
residual block to capture and enhance image details. Subsequent layers
(down2-down5) employ similar structures with an increasing number of output
channels, encouraging the network to learn complex representations. To enhance
the network’s capacity for capturing fine-grained features and context,
Spatial and Channel Attention (SCA) modules are integrated into the
architecture. These modules act as mechanisms for focusing on relevant spatial
regions and channel-wise information, respectively. These attention mechanisms
are integrated at multiple stages in the network to enrich the feature
representations. The decoder upscales the feature maps and fuses information
from the encoder using skip connections. Each decoder block consists of a
transposed convolutional layer to increase spatial resolution, followed by
batch normalization and ReLU activation. The final stage of the network
involves upsampling the feature maps, followed by a convolutional layer with
4x4 filters to produce the enhanced output image with the same dimensions as
the input.

####  3.1.1 Spatio-Channel Attention

Inspired by recent advancements in low-level vision techniques, which rely on
attention mechanisms [33, 34, 35, 36, 37], we utilized the twofold attention
unit named spatio-channel attention (SCA) to extract features in the
convolutional feature maps over multiple streams of information. The schematic
of spatio-channel attention is shown in Fig.3.

The SCA efficiently diminishes less pertinent features, allowing only the
transmission of more informative ones to the next layers. This feature
recalibration is done through the utilization of channel attention [36] and
spatial attention [37] mechanisms.

Figure 3: Spatio-Channel attention module incorporating both channel domain
attention and spatial domain attention.

A channel attention map is generated by exploiting the inter-channel
relationships within feature maps. Each channel of a feature map is treated as
a feature detector. The primary objective of channel attention is to identify
and emphasize the most meaningful information within an input image. To
efficiently compute channel attention, a two-step process is proposed. First,
a ”squeeze” operation is applied to the input feature map, which involves
global average pooling across the spatial dimensions. This step is designed to
capture global context information. As a result of this operation, a feature
descriptor denoted as $D_{CA}$ is obtained. $D_{CA}$ is a tensor with
dimensions $\mathbb{R}^{1\times 1\times C}$, where $C$ represents the number
of channels in the feature map $F$ with spatial dimensions $H\times W$. This
feature descriptor encapsulates important channel-wise information that can be
used to enhance the feature representation.

Subsequently, the ”excitation” operator is applied to $D_{CA}$, by passing it
through a couple of convolutional layers, followed by a sigmoid gating
function. This process generates activation values denoted as $\hat{d_{CA}}$
with dimensions $\mathbb{R}^{1\times 1\times C}$. Finally, the output of the
channel attention (CA) branch is obtained by scaling the original channel
attention map $F$ with the activation values $\hat{d}$. This scaling operation
enhances the relevance of different channels in the feature map, improving the
representation of important features in the network.

A spatial attention map is produced by exploiting the inter-spatial
relationships among features. Unlike channel attention, which emphasizes
’what’ is meaningful within an input, spatial attention is concerned with
’where’ the informative regions are located, offering a complementary
perspective. To create the spatial attention map, the SA branch begins by
separately applying global average pooling and max pooling operations along
the channel dimensions of the feature map $F$. The results of these pooling
operations are concatenated to create a feature descriptor represented as
$D_{SA}$ with dimensions $\mathbb{R}^{H\times W\times 2}$. Subsequently, this
feature descriptor undergoes a convolution operation followed by a sigmoid
activation function. This process yields the spatial attention map, denoted as
$\hat{d_{SA}}$, which has dimensions $\mathbb{R}^{H\times W\times 1}$. This
spatial attention map $\hat{d_{SA}}$ is then multiplied to rescale the
original feature set $F$, contributing to the refinement of feature
representations within the neural network.

###  3.2 Disciminator Architecture

For the discriminator, we adopted the structural paradigm of Markovian
PatchGANs [38]. This structural approach utilizes a patchwise processing of
the input image, yielding a characteristic matrix of dimensions $n\texttimes
n\texttimes 1$. Each element within this matrix corresponds to a distinct
patch in the input image, thus enabling the discrimination of the entire image
by assessing all constituent patches. Notably, this architectural choice rests
upon the assumption of pixel independence beyond the patch size, implying that
the discrimination of images depends solely on patch-level information. This
assumption bears particular significance in effectively capturing high-
frequency features, such as local textures and styles [39]. Furthermore, this
structural design offers computational efficiency benefits by minimizing the
number of parameters through patch-level comparisons. As depicted in Fig, the
discriminator takes both the generated image and the ground truth as input for
identification purposes. Following this, a sequence of five convolutional
layers, coupled with four nonlinear activation functions, is utilized to
render a binary output of real or fake. The initial four layers of the
discriminator employ a 3 × 3 convolutional kernel with a stride of 2, while
the final layer adopts a 4 × 4 convolutional kernel with a stride of 1.
Rectified Linear Unit (ReLU) activation functions are applied to introduce
nonlinearity into the network architecture.

###  3.3 Loss Functions

In this work, we used a linear combination of the adversarial loss fucntion
$L_{a}dv$ , $L_{1}$ loss function and the perceptual loss function
$L_{\text{per}}$.

$L_{\text{total}}=L_{\text{adv}}+\lambda_{1}L_{\text{per}}+\lambda_{2}L_{1}$
(1)

where, $\lambda_{1}$ and $\lambda_{2}$ are scaling factors.

Adversarial Loss: We used adversarial loss to facilitate the training of the
generator network and the discriminator network in a competitive manner. This
loss function encourages the generator to minimize the log probability that
the discriminator assigns to the generated samples, effectively pushing the
generator to produce more enhanced images similar to reference images.
Mathematically, it can be expressed as:

$L_{\text{adv}}(G,D)=\mathbb{E}_{X,Y}\left[\log
D(X,Y)\right]+\mathbb{E}_{X,Z}\left[\log(1-D(X,G(X,Z)))\right]$ (2)

where X represents the input images, Y represents the reference images, and
G(X,Z) represents the generated images.

Perceptual loss: We also used a perceptual loss inspired by the work of
Johnson et al [40], aiming to capture the global dependency between underwater
images and ground truth images by considering deep features. This perceptual
loss is defined based on the VGG-19 network [41], which is a deep
convolutional neural network pre-trained on a large dataset. The perceptual
loss, denoted as $L_{\text{per}}$, quantifies the dissimilarity between
feature representations of the generated underwater image and the ground truth
image as captured by VGG-19. It is calculated as the sum of the $\ell_{1}$
absolute difference between feature maps in a chosen layer of the VGG-19
network. Mathematically, the perceptual loss can be expressed as:

$L_{\text{per}}=\frac{1}{C_{j}H_{j}W_{j}}\sum_{j}\left\|\Phi_{j}({Y})-\Phi_{j}(G(X,Z))\right\|$
(3)

Where, $L_{\text{per}}$ represents the perceptual loss. $j$ refers to the
index of the chosen layer in the VGG-19 network. $C_{j}$, $H_{j}$, and $W_{j}$
are the channel number, height, and width of the feature map in the $j$th
layer of VGG-19. $\Phi_{j}({G(X,Y)})$ is the feature representation of the
generated underwater image $G(X,Y)$ in the $j$th layer. $\Phi_{j}(Y)$ is the
feature representation of the ground truth image $Y$ in the $j$th layer. In
this formulation, the perceptual loss measures the dissimilarity between
feature maps extracted from the VGG-19 network, helping to guide the
optimization process toward generating images that align more closely with the
features of the ground truth images.

MAE loss: We also used the MAE loss (L1 loss) to enhance the model’s ability
to capture fine-grained details while mitigating blurring effects,
contributing to improved sample quality and overall performance. L1 loss can
be mathematically expressed as:

$L_{1}(G)=\mathbb{E}_{X,Y,Z}\left[\left\|Y-G(X,Z)\right\|\right]$ (4)

Where Y represents the reference image in our target domain and $G(X,Z)$
represents the generated image from the generator.

##  4 Experimentation

Datasets: In this study, we experimented with the Underwater Image Enhancement
Benchmark (UIEB) dataset for both training and testing our model. The UIEB
dataset consists of 950 real-world underwater images, out of which 890 images
have corresponding reference images. These original images were captured from
various real-world underwater environments. To create the reference images, 12
different enhancement methods were applied to generate candidate images, and
multiple volunteers participated in voting for each pair of enhanced images.
The image with the highest number of votes was selected as the corresponding
reference image[11]. The UIEB dataset provides an excellent platform for
evaluating diverse underwater image enhancement algorithms, considering its
rich and varied underwater scenes, along with images exhibiting varying
degrees of degradation. For our experimentation, we randomly chose 800 pairs
of real-world images for training. The test dataset comprises the remaining 90
real-world images in the UIEB dataset, 60 challenge images (no reference), and
U45 [42], the underwater color cast set (UCCS) dataset [43].

Experiment Settings:

To ensure a comprehensive evaluation of our model, we conducted qualitative
and quantitative comparison experiments with several state-of-the-art methods.
To maintain consistency, we utilized the source codes provided by the
respective authors or referred to the descriptions in the original papers.
Additionally, for some models, we relied on open-source resources to construct
the necessary models for evaluation. Throughout the experiments, all the
methods underwent the same testing and training processes. The evaluation
environment was set up with an NVIDIA RTX 6000 48GB GPU, Intel Xeon(R) Gold
6230 CPU @ 2.10GHz × 80 to ensure fair and comparable results.

###  4.1 Evaluation Metrics:

In our experiments, we rigorously evaluated multiple models using widely known
underwater image quality evaluation metrics. We evaluated the models
extensively for part of UIEB test dataset which contains reference images
using two core metrics: Peak Signal-to-Noise Ratio (PSNR) [44] and Structural
Similarity Index (SSIM) [45]. These metrics are critical in assessing the
degree of similarity between the image content and structural texture and the
reference image. Higher PSNR and SSIM values indicate more fidelity, implying
a closer alignment with the reference image.

For datasets with no reference images, we utilized specific metrics for
underwater images. These metrics include underwater image quality measures
(UIQM)[46], underwater color image quality evaluation (UCIQE) [47], and
naturalness image quality evaluator (NIQE) [48]. Through these metrics, we
sought to capture crucial aspects related to the consistency of the enhanced
images with human visual perception, the appropriate balance of hue,
saturation, and contrast, and the overall visual quality. Higher UIQM and
UCIQE scores are indicative of closeness to human visual perception, while a
lower NIQE score suggests an elevated level of visual quality in the image.
UCIQE is a composite measure based on chroma $\sigma_{c}$, contrast $con_{l}$,
and saturation $\mu_{s}$, computed as follows:

Figure 4: A comparative visual analysis of SOTA Methods for greenish color
scenes. Left to right columns show, Input image, Reference image, and outputs
of different methods such as Fusion[49], IBLA[50], U-Transformer[51], Funie-
GAN[9], Water-Net[11], UWCNN[10] and Ours Figure 5: A comparative visual
analysis of SOTA Methods for blueish color scenes.Left to right columns show,
Input image, Reference image, and outputs of different methods such as
Fusion[49], IBLA[50], U-Transformer[51], Funie-GAN[9], Water-Net[11],
UWCNN[10] and Ours Figure 6: A comparative visual analysis of SOTA Methods for
yellowish color scenes..Left to right columns show, Input image, Reference
image, and outputs of different methods such as Fusion[49], IBLA[50],
U-Transformer[51], Funie-GAN[9], Water-Net[11], UWCNN[10] and Ours

$UCIQE=c_{1}\cdot\sigma_{c}+c_{2}\cdot con_{l}+c_{3}\cdot\mu_{s}$ (5)

where the coefficients are $c_{1}=0.4680$, $c_{2}=0.2745$, and $c_{3}=0.2575$,
as defined in [47]. A higher UCIQE score indicates an improved balance between
chroma, contrast, and saturation. Meanwhile, UIQM is formed through a linear
combination of UICM, UISM, and UIConM, representing colorfulness, sharpness,
and contrast metrics, respectively:

$UIQM=c_{1}\cdot UICM+c_{2}\cdot UISM+c_{3}\cdot UIConM$ (6)

with coefficients $c_{1}=0.0282$, $c_{2}=0.2953$, and $c_{3}=3.5753$, as
suggested in [46]. A higher UIQM score reflects an enhanced trade-off among
colorfulness, sharpness, and contrast in the evaluated results.

##  5 QUALITATIVE ANALYSIS

The UIEB, a dataset containing a diverse range of degraded underwater images,
serves as a common benchmark for evaluating underwater image enhancement
techniques. Initially, a selection of underwater images from the UIEB was made
based on the visual appearance, and they were categorized into eight
appearance-based groups: greenish, bluish, yellowish, downward-looking,
upward-looking, forward-looking, low backscattered, and high backscattered.

Within open-water environments, the sequence of light attenuation is
distinctive, with red light being the first to diminish due to its longer
wavelength, followed by green light, blue light and lastly yellow light [52].
This selective attenuation characteristic of open water often gives rise to
underwater images exhibiting a yellowish, bluish, or greenish cast, such as
those exhibited in Figure 4-5-6. The resultant color deviation significantly
undermines the visual fidelity of these underwater images, presenting a
challenge in terms of rectification.

As depicted in Figure 4, IBLA did not effectively eliminate the greenish
color, while Fusion resulted in a somewhat grayish tone replacing the green.
Among the deep learning techniques, Funie-GAN successfully removed the green
hue but introduced some flickering noise into the images. On the other hand,
UWCNN was not able to significantly enhance the images. Water-Net,
U-Transformer and our method, however, managed to successfully eliminate the
greenish tint and improve image quality. While there may be some minor color
differences, overall, the image quality has been notably enhanced.

Figure 5 illustrates a visual comparison of bluish color correction. In this
context, IBLA did not significantly contribute to image enhancement, and
Fusion introduced a form of haze. Among the deep learning-based methods, UWCNN
performed the poorest. Water-Net retained some bluish content in the resulting
images, whereas Funie-GAN, U-Transformer, and our approach successfully
enhanced the images and effectively corrected the color.

In Figure 6, observations reveal that IBLA has introduced a bluish tint in a
few images. Fusion has struggled to enhance contrast in the image. Among the
deep learning-based methods, Water-Net introduced a bluish tint in one image,
and UWCNN has darkened the overall visual appearance. Funie-GAN still exhibits
some residual yellowish artifacts, while U-Transformer and our method has
effectively eliminated the yellowish haze.

Hence, when it comes to color correction, our method has consistently
outperformed other approaches. While various methods exhibited strengths and
weaknesses in addressing color issues, our method consistently delivered
superior results, making it the most effective choice for color correction.

Figure 7: A comparative visual analysis of SOTA Methods for downward-looking
scenes.Left to right columns show, Input image, Reference image, and outputs
of different methods such as Fusion[49], IBLA[50], U-Transformer[51], Funie-
GAN[9], Water-Net[11], UWCNN[10] and Ours Figure 8: A comparative visual
analysis of SOTA methods for upward-looking scenes. Left to right columns
show, Input image, Reference image, and outputs of different methods such as
Fusion[49], IBLA[50], U-Transformer[51], Funie-GAN[9], Water-Net[11],
UWCNN[10] and Ours Figure 9: A comparative visual analysis of SOTA Methods for
forward-looking scenes. Left to right columns show, Input image, Reference
image, and outputs of different methods such as Fusion[49], IBLA[50],
U-Transformer[51], Funie-GAN[9], Water-Net[11], UWCNN[10] and Ours

In the context of scene perspective from various angles, downward-looking,
upward-looking, and forward-looking images are illustrated in Figures 7, 8,
and 9.

In Figure 7, Fusion has impressively mitigated the impact of haze on
underwater images. Notably, methods such as IBLA, and UWCNN tended to
introduce color distortions in their enhanced outputs. While Water-Net still
retains some remnants of the hazy effect, other methods have shown significant
improvements in haze removal.

Moreover, in Figure 8, methods such as Fusion, IBLA, and UWCNN faced
difficulties in accurately estimating veiling light from upward-looking
images. These challenges extended to inaccuracies in estimating veiling light
from RGB values, especially in textured regions of upward-looking images.
While Funie-GAN, U-Transformer and Water-Net exhibited some remaining color
deviations, Funie-GAN also introduced blurriness. Overall, our method has
demonstrated superior performance compared to all other methods.

In forward-looking images, as depicted in Figure 9, it’s worth noting that,
except for IBLA and UWCNN, all other methods have shown significant
improvements in enhancing image quality. Overall, our proposed method
demonstrated exceptional performance by achieving optimal exposure and
intricate detail restoration across all three angles.

Figure 10: A comparative visual analysis of SOTA Methods for low-back scattered scenes.Left to right columns show, Input image, Reference image, and outputs of different methods such as Fusion[49], IBLA[50], U-Transformer[51], Funie-GAN[9], Water-Net[11], UWCNN[10] and Ours Figure 11: A comparative visual analysis of SOTA Methods for high backscattered scenes. Left to right columns show, Input image, Reference image, and outputs of different methods such as Fusion[49], IBLA[50], U-Transformer[51], Funie-GAN[9], Water-Net[11], UWCNN[10] and Ours Table 1: Full-reference image quantitative comparison on UIEB test dataset by using PSNR and SSIM. Method | Fusion [49] | IBLA [50] | U-Transformer [51] | Funie-GAN [9] | Water-Net [11] | UWCNN [10] | Ours
---|---|---|---|---|---|---|---
PSNR | 20.87 | 19.04 | 21.64 | 21.03 | 24.36 | 15.25 | 25.59
SSIM | 0.857 | 0.703 | 0.778 | 0.775 | 0.885 | 0.656 | 0.893

Scattering induces intricate alterations in the trajectory of light
propagation, thereby influencing the spatial and temporal distribution of
light energy. Within underwater images, the scattering phenomenon is
contingent on the distance between the scene and the camera. As evident in
Figure 10, mitigating the effects of fog is relatively straightforward in low-
scatter scenes characterized by short distances. However, in such scenes, IBLA
and UWCNN have introduced unwanted darkness, while Funie-GAN introduced
blurriness. On the other hand, Fusion, Water-Net, U-Transformer and our method
have excelled in enhancing image quality under these conditions.

Conversely, Figure 11 illustrates the difficulty in rectifying blur effects
prevalent in high-scatter scenes that entail greater distances. The challenge
becomes apparent in the context of highly scattered scenes, as inaccuracies in
models hinder the capacity of physical model-based methods (such as Fusion and
IBLA) to eliminate scattering, as observed in Figure 11. In contrast, Funei-
GAN, water-net, U-Transformer, and our technique exhibit superior performance
in addressing this issue. Under conditions of extreme degradation,
characterized by weak illumination and substantial scattering of suspended
particles, underwater images contend with issues like color distortion,
reduced contrast, and blurriness. These combined factors pose a considerable
challenge in the realm of underwater image enhancement. Despite these
formidable challenges, our model has consistently excelled in enhancing image
quality.

##  6 Quantitative Analysis

To quantitatively evaluate the performance of different methods, we conduct
both full-reference and non-reference evaluations.

###  6.1 Full-reference Evaluation

In the evaluation of the test datasets containing reference images, a full-
reference assessment is conducted utilizing two commonly-utilized metrics:
PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index. These
metrics enable an objective evaluation of the method’s performance. Higher
PSNR and SSIM scores indicate a closer resemblance of the result to the
reference image.

As shown in Table 1. IBLA achieved moderate results while Fuison performed
comparatively better with 20.87, and 0.857 and for PSNR score and SSIM,
respectively, indicating good performance in noise reduction and structural
preservation. While UWCNN faced challenges in image enhancement, with a PSNR
of 15.25 and an SSIM of 0.656, indicating limited noise reduction and
structural preservation, other deep learning-based methods have performed
quite well. U-Transformer and Funie-GAN delivered better results with a
commendable PSNR of 21.64 and 21.03 respectively, and an SSIM of 0.778 and
0.775 respectively, signifying reduced distortion and substantial structural
similarity. Water-Net and Ours exhibited outstanding results. Our method
outperformed Water-Net, achieving a higher PSNR of 25.59 compared to Water-
Net’s 24.36, and achieving the highest SSIM value of 0.893 compared to Water-
Net’s 0.885.

###  6.2 Non-Reference Evaluation

In the evaluation of test datasets that do not contain reference images, three
non-reference metrics, namely UCIQE [47] and UIQM [46], and NIQE [48] are
used. A higher score in either UCIQE or UIQM signifies an enhanced level of
human visual perception in the results. while a lesser score in NIQE indicates
a better perception quality. In the evaluation of the C-60 challenge dataset,
our method demonstrated remarkable performance across various quality metrics.
As shown in Table 2. our method achieved the highest UIQM score of 3.12,
surpassing all other approaches. Funie-GAN achieved the second-highest UIQM
score of 3.10. When considering a balance between chroma, contrast, and
saturation, our method once again excelled with the highest UCIQE score of
0.591, followed closely by Water-Net with a score of 0.578. In terms of
perception quality, our method outperformed all others, achieving a NIQE value
of 4.67, while Funie-GAN attained a value of 4.73.

Table 3. shows the assessment of the U45 dataset, our method attained a UIQM
score of 3.23 and a UCIQE score of 0.612, making it the top performer in terms
of image quality enhancement. Funie-GAN emerged as the second-best method with
respective scores of 3.21 for UIQM and 0.602 for UCIQE. Moreover, when
evaluating the perception quality, our method demonstrated its superiority
once more, achieving a NIQE score of 3.86. Water-Net closely followed with a
NIQE score of 3.91. As shown in Table 5, our method achieved a UIQM score of
3.17 and a UCIQE score of 0.568, showcasing its effectiveness in enhancing
image quality for UCCS dataset. Water-Net also demonstrated strong performance
with a UIQM score of 3.13, while Funie-GAN achieved a competitive UCIQE score
of 0.558. In terms of perception quality, the U-Transformer received an NIQE
score of 3.96, with our method closely following with a score of 4.03.

Table 2: Quantitative comparison on C-60 dataset using UIQM, UCIQE, and NIQE metrics. | UIQM$\uparrow$ | UCIQE$\uparrow$ | NIQE$\downarrow$
---|---|---|---
Raw_Images | 1.99 | 0.478 | 5.18
Fusion | 2.78 | 0.512 | 4.74
IBLA | 1.81 | 0.574 | 5.02
U-Transformer | 2.65 | 0.534 | 4.94
Funie-GAN | 3.10 | 0.572 | 4.73
Water-Net | 2.57 | 0.578 | 4.88
UWCNN | 2.25 | 0.466 | 4.89
Ours | 3.12 | 0.591 | 4.67
Table 3: Quantitative comparison on U45 dataset using UIQM, UCIQE, and NIQE metrics. | UIQM$\uparrow$ | UCIQE$\uparrow$ | NIQE$\downarrow$
---|---|---|---
Raw_Images | 2.44 | 0.481 | 5.03
Fusion | 3.14 | 0.532 | 3.97
IBLA | 1.60 | 0.579 | 5.11
U-Transformer | 3.10 | 0.553 | 4.30
Funie-GAN | 3.21 | 0.602 | 3.95
Water-Net | 3.18 | 0.587 | 3.91
UWCNN | 2.82 | 0.471 | 4.52
Ours | 3.23 | 0.612 | 3.86
Table 4: Quantitative comparison on UCCS dataset using UIQM, UCIQE, and NIQE metrics. | UIQM$\uparrow$ | UCIQE$\uparrow$ | NIQE$\downarrow$
---|---|---|---
Raw_Images | 2.29 | 0.410 | 4.57
Fusion | 2.99 | 0.476 | 4.38
IBLA | 2.36 | 0.480 | 4.29
U-Transformer | 3.02 | 0.539 | 3.96
Funie-GAN | 3.05 | 0.558 | 4.37
Water-Net | 3.13 | 0.550 | 4.07
UWCNN | 2.78 | 0.455 | 4.12
Ours | 3.17 | 0.568 | 4.03

Overall, deep learning-based methods demonstrated superior performance
compared to prior-based methods in enhancing the quality of images in the
mentioned datasets. Among these, our method emerged as the top-performing one,
showcasing its effectiveness in improving image quality and perceptual
fidelity, making it a valuable choice for enhancing images.

##  7 Evaluation on locally collected dataset

In this study, we also validated our proposed method on our locally collected
dataset for underwater bio-fouling detection in an aquaculture environment at
Saadiyat Beach, Abu Dhabi. Primarily, the dataset contains images of fish and
biofouling grown on the aqua-net surface [53, 54]. These images were acquired
using the Blueye ROV as shown in Figure 12. The ROV has an installed HD
camera, which allows the recording of high-resolution images of size 1080x1920
pixels. A total of 159 images are selected by getting 1 frame per 2 seconds.
This comprehensive evaluation covered a wide range of scenes, encompassing
various lighting conditions, scattering effects, and perspective views. As
shown in Fig. 12, our method consistently demonstrated excellent performance
by effectively removing haze and enhancing images across these diverse
scenarios.

Figure 12: Data collection. (A) shows Blueye ROV used for data collection,
(B), (C), and (D) show the different scenes captured during the experiments.

On the other hand, among the prior-based methods, Fusion exhibited better
performance by mitigating the haze effect, although it fell short of achieving
complete image enhancement. In contrast, IBLA introduced noticeable color
deviations rather than enhancement, which was less desirable. Among the deep
learning-based methods, Funie-GAN showcased strong performance but tended to
introduce noise artifacts into the images. UWCNN, however, struggled as it
darkened the resultant images, impacting overall quality. U-Transformer and
Water-Net delivered satisfactory results, although some residual haze
persisted in the output images.

Figure 13: Visual comparison of different methods on the private dataset.Left
to right columns show, Input image, Reference image, and outputs of different
methods such as Fusion[49], IBLA[50], U-Transformer[51], Funie-GAN[9], Water-
Net[11], UWCNN[10] and Ours Table 5: Non-reference image quantitative
comparison on private data using UIQM, UCIQE, and NIQE.

| UIQM$\uparrow$ | UCIQE$\uparrow$ | NIQE$\downarrow$
---|---|---|---
Fusion | 2.92 | 0.545 | 5.40
IBLA | 1.47 | 0.626 | 6.38
U-Transformer | 2.99 | 0.55 | 5.21
Funie-GAN | 3.21 | 0.599 | 5.56
Water-Net | 3.02 | 0.582 | 6.06
UWCNN | 2.59 | 0.562 | 5.75
Ours | 3.26 | 0.603 | 5.39

A quantitative analysis of this dataset is given in table 3. confirms the
effectiveness of our model. Our method has achieved a UIQM score of 3.26 and
an NIQE score of 5.39, followed by the Funie-GAN in terms of UIQM with a score
of 3.21 and Fusion in terms of NIQE with a score of 5.40. While measuring
UCIQE score, IBLA has achieved a score of 0.626, although their resultant
images are just the color deviations from the original images. But since UCIQE
measures a linear combination of chroma, saturation, and contrast, a higher
value can be expected just by having a change in colors (even if they are not
desirable).

##  8 Conclusion

In this study, we introduced an innovative GAN-based architecture designed
specifically for underwater image enhancement applications. A key advancement
is the incorporation of spatial and channel attention within the generator’s
encoder, contributing to enhanced feature representation learning and
subsequent improvements in image enhancement performance. Our comprehensive
experiments, spanning diverse datasets including the UIEB test dataset, UIEB
challenge dataset, U45, and UCCS dataset, demonstrated the superior
performance of our model both qualitatively and quantitatively. Notably, the
proposed model exhibited a significant performance boost, surpassing Water-Net
by 5$\%$ in PSNR value and achieving more than a 1$\%$ improvement in SSIM for
the reference image test dataset. Furthermore, the validation of our trained
model on a private underwater imaging dataset acquired from an open sea
environment under various conditions showcased impressive performance. This
underscores the substantial potential of our model for real-time deployment on
underwater vehicles. In conclusion, our work contributes to the field of
underwater image enhancement by introducing a novel GAN-based model with
integrated spatial and channel attention mechanisms. The demonstrated
improvements across controlled datasets and real-world conditions underscore
the robustness and practical applicability of our proposed model, marking a
noteworthy advancement in underwater imaging technologies.

## Acknowledgement

This work is supported by the Khalifa University of Science and Technology
under Award No. MBZIRC-8434000194, CIRA-2021-085, FSU-2021-019,
RC1-2018-KUCARS, 8434000534.

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