Underwater image enhancement (UIE) poses challenges due to distinctive properties of the underwater environment, including low contrast, high turbidity, visual blurriness, and color distortion. In recent years, the application of deep learning has quietly revolutionized various areas of scientific research, including UIE. However, existing deep learning-based UIE methods generally suffer from issues of weak robustness and limited adaptability. In this paper, inspired by residual and attention mechanisms, we propose a more reliable and reasonable UIE network called RAUNE-Net by employing residual learning of high-level features at the network's bottle-neck and two aspects of attention manipulations in the down-sampling procedure. Furthermore, we collect and create two datasets specifically designed for evaluating UIE methods, which contains different types of underwater distortions and degradations. The experimental validation demonstrates that our method obtains promising objective performance and consistent visual results across various real-world underwater images compared to other eight UIE methods. Our example code and datasets are publicly available at https://github.com/fansuregrin/RAUNE-Net.
University of Chinese Academy of Sciences（中国科学院大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；CAS Center for Excellence in Brain Science and Intelligence Technology（中国科学院脑科学与智能技术卓越创新中心）
Semantic segmentation of surgical instruments plays a crucial role in robot-assisted surgery. However, accurate segmentation of cataract surgical instruments is still a challenge due to specular reflection and class imbalance issues. In this paper, an attention-guided network is proposed to segment the cataract surgical instrument. A new attention module is designed to learn discriminative features and address the specular reflection issue. It captures global context and encodes semantic dependencies to emphasize key semantic features, boosting the feature representation. This attention module…
We present a wavelet-based dual-stream network that addresses color cast and blurry details in underwater images. We handle these artifacts separately by decomposing an input image into multiple frequency bands using discrete wavelet transform, which generates the downsampled structure image and detail images. These sub-band images are used as input to our dual-stream network that incorporates two sub-networks: the multi-color space fusion network and the detail enhancement network. The multi-color space fusion network takes the decomposed structure image as input and estimates the color corre…
Nanjing University of Information Science and Technology（南京信息工程大学）
Underwater image enhancement algorithms have attracted much attention in underwater vision task. However, these algorithms are mainly evaluated on different data sets and different metrics. In this paper, we set up an effective and pubic underwater test dataset named U45 including the color casts, low contrast and haze-like effects of underwater degradation and propose a fusion adversarial network for enhancing underwater images. Meanwhile, the well-designed the adversarial loss including Lgt loss and Lfe loss is presented to focus on image features of ground truth, and image features of the i…
DACA-Net: A Degradation-Aware Conditional Diffusion Network for Underwater Image Enhancement
The Hong Kong University of Science and Technology (Guangzhou), Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)（香港科学与技术大学（广州）、广东海洋工程科学实验室（广州））；The Hong Kong University of Science（香港科学与技术大学）；Engineering Guangdong Laboratory (Guangzhou)（广东工程实验室（广州））
Underwater visual perception is essentially important for underwater exploration, archeology, ecosystem and so on. The low illumination, light reflections, scattering, absorption and suspended particles inevitably lead to the critically degraded underwater image quality, which causes great challenges on recognizing the objects from the underwater images. The existing underwater enhancement methods that aim to promote the underwater visibility, heavily suffer from the poor image restoration performance and generalization ability. To reduce the difficulty of underwater image enhancement, we intr…
National University of Singapore（新加坡国立大学）；Wuhan University（武汉大学）；Chinese University of Hong Kong（香港中文大学）
Color correction for underwater images has received increasing interests, due to its critical role in facilitating available mature vision algorithms for underwater scenarios. Inspired by the stunning success of deep convolutional neural networks (DCNNs) techniques in many vision tasks, especially the strength in extracting features in multiple scales, we propose a deep multi-scale feature fusion net based on the conditional generative adversarial network (GAN) for underwater image color correction. In our network, multi-scale features are extracted first, followed by augmenting local features…
Jimei University（集美大学）；National University of Singapore（新加坡国立大学）；Southwest University（西南大学）；Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing（福建省海洋信息感知与智能处理重点实验室）
Underwater images typically experience mixed degradations of brightness and structure caused by the absorption and scattering of light by suspended particles. To address this issue, we propose a Real-time Spatial and Frequency Domains Modulation Network (RSFDM-Net) for the efficient enhancement of colors and details in underwater images. Specifically, our proposed conditional network is designed with Adaptive Fourier Gating Mechanism (AFGM) and Multiscale Convolutional Attention Module (MCAM) to generate vectors carrying low-frequency background information and high-frequency detail features, …
Ocean University of China（中国海洋大学）；Qingdao University（青岛大学）；China University of Geosciences（中国地质大学）
Due to the wavelength-dependent light attenuation, refraction and scattering, underwater images usually suffer from color distortion and blurred details. However, due to the limited number of paired underwater images with undistorted images as reference, training deep enhancement models for diverse degradation types is quite difficult. To boost the performance of data-driven approaches, it is essential to establish more effective learning mechanisms that mine richer supervised information from limited training sample resources. In this paper, we propose a novel underwater image enhancement net…
PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Images
Graduate School of Engineering, The University of Osaka（大阪大学工学研究科）；Graduate School of BASE, Tokyo University of Agriculture and Technology（东京大学农业技术大学院大学）
This work proposes a method for underwater image enhancement using the principle of histogram equalization. Since underwater images have a global strong dominant colour, their colourfulness and contrast are often degraded. Before applying the histogram equalisation technique on the image, the image is converted from coloured image to a gray scale image for further operations. Histogram equalization is a technique for adjusting image intensities to enhance contrast. The colours of the image are retained using a convolutional neural network model which is trained by the datasets of underwater im…
IDA-UIE: An Iterative Framework for Deep Network-based Degradation Aware Underwater Image Enhancement
Centre for Intelligent Cyber Physical Systems, Indian Institute of Technology Guwahati（印度古瓦哈蒂理工学院智能信息物理系统中心）；Dept. of Electronics and Electrical Engg., Indian Institute of Technology Guwahati（印度古瓦哈蒂理工学院电子与电气工程系）
Underwater Image Enhancement (UIE) technology aims to tackle the challenge of restoring the degraded underwater images due to light absorption and scattering. To address problems, a novel U-Net based Reinforced Swin-Convs Transformer for the Underwater Image Enhancement method (URSCT-UIE) is proposed. Specifically, with the deficiency of U-Net based on pure convolutions, we embedded the Swin Transformer into U-Net for improving the ability to capture the global dependency. Then, given the inadequacy of the Swin Transformer capturing the local attention, the reintroduction of convolutions may c…
Indian Institute of Technology Guwahati（印度理工学院古瓦哈提分校）
Background: Underwater images, in general, suffer from low contrast and high color distortions due to the non-uniform attenuation of the light as it propagates through the water. In addition, the degree of attenuation varies with the wavelength resulting in the asymmetric traversing of colors. Despite the prolific works for underwater image restoration (UIR) using deep learning, the above asymmetricity has not been addressed in the respective network engineering. Contributions: As the first novelty, this paper shows that attributing the right receptive field size (context) based on the travers…
Data61, CSIRO（联邦科学与工业研究组织Data61）；Australian National University（澳大利亚国立大学）；City University of Hong Kong（香港城市大学）
The powerful representation capacity of deep learning has made it inevitable for the underwater image enhancement community to employ its potential. The exploration of deep underwater image enhancement networks is increasing over time, and hence; a comprehensive survey is the need of the hour. In this paper, our main aim is two-fold, 1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and 2): to conduct a qualitative and quantitative comparison of the deep algorithm…
Underwater image enhancement has attracted much attention due to the rise of marine resource development in recent years. Benefit from the powerful representation capabilities of Convolution Neural Networks(CNNs), multiple underwater image enhancement algorithms based on CNNs have been proposed in the last few years. However, almost all of these algorithms employ RGB color space setting, which is insensitive to image properties such as luminance and saturation. To address this problem, we proposed Underwater Image Enhancement Convolution Neural Network using 2 Color Space (UICE^2-Net) that eff…
ASUS Intelligent Cloud Services, Asustek Computer Inc.（华硕电脑股份有限公司智能云服务）；National Taiwan University（国立台湾大学）
Underwater image enhancement is an important low-level computer vision task for autonomous underwater vehicles and remotely operated vehicles to explore and understand the underwater environments. Recently, deep convolutional neural networks (CNNs) have been successfully used in many computer vision problems, and so does underwater image enhancement. There are many deep-learning-based methods with impressive performance for underwater image enhancement, but their memory and model parameter costs are hindrances in practical application. To address this issue, we propose a lightweight adaptive f…
The degradation in the underwater images is due to wavelength-dependent light attenuation, scattering, and to the diversity of the water types in which they are captured. Deep neural networks take a step in this field, providing autonomous models able to achieve the enhancement of underwater images. We introduce Underwater Capsules Vectors GAN UWCVGAN based on the discrete features quantization paradigm from VQGAN for this task. The proposed UWCVGAN combines an encoding network, which compresses the image into its latent representation, with a decoding network, able to reconstruct the enhancem…
Australian National University（澳大利亚国立大学）；Civil Aviation University of China（中国民航大学）
In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts. To address this problem, we propose a convolutional neural network based image enhancement model, i.e., UWCNN, which is trained efficiently using a synthetic underwater image database. Unlike the existing works that require the parameters of underwater imaging model estimation or impose inflexible frameworks applicable only for specific scenes, our model directly reconstructs the clear latent underwater image by leveraging on an automati…
City University of Hong Kong（香港城市大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；Tianjin University（天津大学）；Beijing Jiaotong University（北京交通大学）
Underwater image enhancement has been attracting much attention due to its significance in marine engineering and aquatic robotics. Numerous underwater image enhancement algorithms have been proposed in the last few years. However, these algorithms are mainly evaluated using either synthetic datasets or few selected real-world images. It is thus unclear how these algorithms would perform on images acquired in the wild and how we could gauge the progress in the field. To bridge this gap, we present the first comprehensive perceptual study and analysis of underwater image enhancement using large…
Most deep models for underwater image enhancement resort to training on synthetic datasets based on underwater image formation models. Although promising performances have been achieved, they are still limited by two problems: (1) existing underwater image synthesis models have an intrinsic limitation, in which the homogeneous ambient light is usually randomly generated and many important dependencies are ignored, and thus the synthesized training data cannot adequately express characteristics of real underwater environments; (2) most of deep models disregard lots of favorable underwater prior…
Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement
School of Computer Science and Technology, Dalian University of Technology（大连理工大学计算机科学与技术学院）；University of New South Wales（新南威尔士大学）；School of Computer Science and Technology, Shandong Technology and Business University（山东工商学院计算机科学与技术学院）；School of Information and Communication Engineering, Dalian Minzu University（大连民族大学信息与通信工程学院）；School of Innovation Experiment, Dalian University of Technology（大连理工大学创新实验学院）
Five A$^{+}$ Network: You Only Need 9K Parameters for Underwater Image Enhancement
National University of Singapore（新加坡国立大学）；ZJU-UIUC Institute, Zhejiang University（浙江大学ZJU-UIUC研究院）；Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing（福建省海洋信息感知与智能处理重点实验室）
City University of Hong Kong（香港城市大学）；Data61, CSIRO（澳大利亚联邦科学与工业研究组织Data61）；Australian National University（澳大利亚国立大学）；Beijing Jiaotong University（北京交通大学）；Nankai University（南开大学）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
Underwater images suffer from color casts and low contrast due to wavelength- and distance-dependent attenuation and scattering. To solve these two degradation issues, we present an underwater image enhancement network via medium transmission-guided multi-color space embedding, called Ucolor. Concretely, we first propose a multi-color space encoder network, which enriches the diversity of feature representations by incorporating the characteristics of different color spaces into a unified structure. Coupled with an attention mechanism, the most discriminative features extracted from multiple c…
Underwater images are inevitably affected by color distortion and reduced contrast. Traditional statistic-based methods such as white balance and histogram stretching attempted to adjust the imbalance of color channels and narrow distribution of intensities a priori thus with limited performance. Recently, deep-learning-based methods have achieved encouraging results. However, the involved complicate architecture and high computational costs may hinder their deployment in practical constrained platforms. Inspired by above works, we propose a statistically guided lightweight underwater image en…
Underwater Image Enhancement with Cascaded Contrastive Learning
College of Science and Technology, Ningbo University（宁波大学科学技术学院）；School of Information Science and Engineering, Ningbo University（宁波大学信息科学与工程学院）；Hangzhou DtDream Science and Technology Ltd.（杭州大梦想科技有限公司）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）
This paper reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline used for color correction of monocular underwater images. Cameras onboard autonomous and remotely operated vehicles can capture high resolution images to map the seafloor, however, underwater image formation is subject to the complex process of light propagation through the water column. The raw images retrieved are characteristically different than images taken in air due to effects such as absorption and scattering,…
Kyushu Institute of Technology（九州工业大学）；Chinese Academy of Science（中国科学院）；Yangzhou University（扬州大学）；Nanjing Normal University（南京师范大学）；Huazhong University of Science and Technology（华中科技大学）
Underwater cameras are widely used to observe the sea floor. They are usually included in autonomous underwater vehicles, unmanned underwater vehicles, and in situ ocean sensor networks. Despite being an important sensor for monitoring underwater scenes, there exist many issues with recent underwater camera sensors. Because of lights transportation characteristics in water and the biological activity at the sea floor, the acquired underwater images often suffer from scatters and large amounts of noise. Over the last five years, many methods have been proposed to overcome traditional underwater…
A Semi-supervised Physics-Aware Triple-Stream Underwater Image Enhancement Network
State Key Laboratory of Physical Oceanography and the Faculty of Information Science and Engineering, Ocean University of China（物理海洋学国家重点实验室和中国海洋大学信息科学与工程学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen university（中山大学深圳校区计算机科学与技术学院）；Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology（深圳视觉目标检测与识别重点实验室，哈尔滨工业大学）
FDCE-Net: Underwater Image Enhancement with Embedding Frequency and Dual Color Encoder
College of Computer Science & Technology, Qingdao University（青岛大学计算机科学与技术学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
JDPNet: A Network Based on Joint Degradation Processing for Underwater Image Enhancement
School of Mechanical and Electrical Engineering, China University of Mining and Technology（中国矿业大学（北京）机械与电子工程学院）；China Shipbuilding Science Research Center（中国船舶科学研究院）；School of Physics and Optoelectronic Engineering, Foshan University（佛山大学物理与电子工程学院）
A main challenge faced in the deep learning-based Underwater Image Enhancement (UIE) is that the ground truth high-quality image is unavailable. Most of the existing methods first generate approximate reference maps and then train an enhancement network with certainty. This kind of method fails to handle the ambiguity of the reference map. In this paper, we resolve UIE into distribution estimation and consensus process. We present a novel probabilistic network to learn the enhancement distribution of degraded underwater images. Specifically, we combine conditional variational autoencoder with …
Recently, learning-based algorithms have shown impressive performance in underwater image enhancement. Most of them resort to training on synthetic data and achieve outstanding performance. However, these methods ignore the significant domain gap between the synthetic and real data (i.e., interdomain gap), and thus the models trained on synthetic data often fail to generalize well to real underwater scenarios. Furthermore, the complex and changeable underwater environment also causes a great distribution gap among the real data itself (i.e., intra-domain gap). However, almost no research focus…
Style-Decoupled Adaptive Routing Network for Underwater Image Enhancement
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University（测绘遥感信息工程国家重点实验室，武汉大学）；Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（航空工程系，香港理工大学）
University of Minnesota Twin Cities（明尼苏达大学双城分校）；Minnesota Robotics Institute（明尼苏达机器人研究所）
In this paper, we introduce a generative model for image enhancement specifically for improving diver detection in the underwater domain. In particular, we present a model that integrates generative adversarial network (GAN)-based image enhancement with the diver detection task. Our proposed approach restructures the GAN objective function to include information from a pre-trained diver detector with the goal to generate images which would enhance the accuracy of the detector in adverse visual conditions. By incorporating the detector output into both the generator and discriminator networks, …
CARNet: Collaborative Adversarial Resilience for Robust Underwater Image Enhancement and Perception
School of Software Technology, Dalian University of Technology（大连理工大学软件学院）；College of Information Science and Technology, Dalian Maritime University（大连海事大学信息科学技术学院）；School of Mechanical Engineering, Dalian University of Technology（大连理工大学机械工程学院）
Underwater image enhancement has become an attractive topic as a significant technology in marine engineering and aquatic robotics. However, the limited number of datasets and imperfect hand-crafted ground truth weaken its robustness to unseen scenarios, and hamper the application to high-level vision tasks. To address the above limitations, we develop an efficient and compact enhancement network in collaboration with a high-level semantic-aware pretrained model, aiming to exploit its hierarchical feature representation as an auxiliary for the low-level underwater image enhancement. Specifical…
Due to the absorption and scattering effects of the water, underwater images tend to suffer from many severe problems, such as low contrast, grayed out colors and blurring content. To improve the visual quality of underwater images, we proposed a novel enhancement model, which is a trainable end-to-end neural model. Two parts constitute the overall model. The first one is a non-parameter layer for the preliminary color correction, then the second part is consisted of parametric layers for a self-adaptive refinement, namely the channel-wise linear shift. For better details, contrast and colorfu…
