Underwater visual imaging is crucial for marine engineering, but it suffers from low contrast, blurriness, and color degradation, which hinders downstream analysis. Existing underwater image enhancement methods often treat the haze and color cast as a unified degradation process, neglecting their inherent independence while overlooking their synergistic relationship. To overcome this limitation, we propose a Vision Transformer (ViT)-based network (referred to as WaterFormer) to improve underwater image quality. WaterFormer contains three major components: a dehazing block (DehazeFormer Block) to capture the self-correlated haze features and extract deep-level features, a Color Restoration Block (CRB) to capture self-correlated color cast features, and a Channel Fusion Block (CFB) that dynamically integrates these decoupled features to achieve comprehensive enhancement. To ensure authenticity, a soft reconstruction layer based on the underwater imaging physics model is included. Further, a Chromatic Consistency Loss and Sobel Color Loss are designed to respectively preserve color fidelity and enhance structural details during network training. Comprehensive experimental results demonstrate that WaterFormer outperforms other state-of-the-art methods in enhancing underwater images.
Underwater Image Restoration via Polymorphic Large Kernel CNNs
University of Macau（澳门大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen Polytechnic University（深圳职业技术大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Fuzhou University（福州大学）；University of Chinese Academy of Sciences（中国科学院大学）；Peking University（北京大学）
School of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）；Institute of Computer Science and Technology, Peking University（北京大学计算机科学技术研究所）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）
The recent physical model-free dehazing methods have achieved state-of-the-art performances. However, without the guidance of physical models, the performances degrade rapidly when applied to real scenarios due to the unavailable or insufficient data problems. On the other hand, the physical model-based methods have better interpretability but suffer from multi-objective optimizations of parameters, which may lead to sub-optimal dehazing results. In this paper, a progressive residual learning strategy has been proposed to combine the physical model-free dehazing process with reformulated scatt…
Academy for Engineering & Technology, Fudan University（复旦大学工程与应用技术研究院）；College of Information Technology, Shanghai Ocean University（上海海洋大学信息学院）；University of Calabria（卡拉布里亚大学）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；Napier Edinburgh University（爱丁堡龙比亚大学）
Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes o…
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…
Tianjin University（天津大学）；Australian National University（澳大利亚国立大学）；Agency for Science, Technology and Research（新加坡科技研究局）
Images captured under outdoor scenes usually suffer from low contrast and limited visibility due to suspended atmospheric particles, which directly affects the quality of photos. Despite numerous image dehazing methods have been proposed, effective hazy image restoration remains a challenging problem. Existing learning-based methods usually predict the medium transmission by Convolutional Neural Networks (CNNs), but ignore the key global atmospheric light. Different from previous learning-based methods, we propose a flexible cascaded CNN for single hazy image restoration, which considers the m…
Optimal Transport Based Unsupervised Restoration Learning Exploiting Degradation Sparsity
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University, China（信息科学与电子工程学院，上海交通大学）；School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China（通信工程学院，杭州电子科技大学）
6thGrid-Net: Unified Remote Sensing Image Dehazing Based on Color Restoration and Edge-Preserving
Harbin Institute of Technology（哈尔滨工业大学）；Nanjing University of Science and Technology（南京理工大学）；National University of Defense Technology（国防科技大学）；Shandong Normal University（山东师范大学）；Qilu University of Technology（齐鲁工业大学）
Central South University（中南大学）；Beijing Jiaotong University（北京交通大学）
Image-to-image translation based on generative adversarial network (GAN) has achieved state-of-the-art performance in various image restoration applications. Single image dehazing is a typical example, which aims to obtain the haze-free image of a haze one. This paper concentrates on the challenging task of single image dehazing. Based on the atmospheric scattering model, a novel model is designed to directly generate the haze-free image. The main challenge of image dehazing is that the atmospheric scattering model has two parameters, i.e., transmission map and atmospheric light. When they are…
Shenyang Aerospace University（沈阳航空航天大学）；Nanjing University of Science and Technology（南京理工大学）；Dalian Polytechnic University（大连工业大学）
We offer a practical unpaired learning based image dehazing network from an unpaired set of clear and hazy images. This paper provides a new perspective to treat image dehazing as a two-class separated factor disentanglement task, i.e, the task-relevant factor of clear image reconstruction and the task-irrelevant factor of haze-relevant distribution. To achieve the disentanglement of these two-class factors in deep feature space, contrastive learning is introduced into a CycleGAN framework to learn disentangled representations by guiding the generated images to be associated with latent factor…
WDMamba: When Wavelet Degradation Prior Meets Vision Mamba for Image Dehazing
School of Computer Science and Technology, Anhui University of Technology（安徽理工大学计算机科学与技术学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；College of Artificial Intelligence, Taiyuan University of Technology（太原科技大学人工智能学院）
Bridging the Gap Between Image Restoration and Navigational Safety in Hazy Conditions: A New Visibility Estimation Metric for Maritime Surveillance
Sanya Science and Education Innovation Park, Wuhan University of Technology, Sanya 572000, China（武汉理工大学三亚科学教育创新园，三亚572000，中国）；School of Navigation, Wuhan University of Technology, Wuhan 430063, China（武汉理工大学航海学院，武汉430063，中国）；State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063, China（武汉理工大学海洋技术与安全国家重点实验室，武汉430063，中国）；Department of Mechanics and Maritime Sciences, Chalmers University of Technology, Göteborg 41296, Sweden（楚科奇大学机械与航海科学系，哥德堡41296，瑞典）
TIDE: Two-Stage Inverse Degradation Estimation with Guided Prior Disentanglement for Underwater Image Restoration
Mohamed bin Zayed University of AI（莫扎德·本·泽亚德人工智能大学）；University of Amsterdam（阿姆斯特丹大学）；University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）；Nagasaki University（长崎大学）
We present an image dehazing algorithm with high quality, wide application, and no data training or prior needed. We analyze the defects of the original dehazing model, and propose a new and reliable dehazing reconstruction and dehazing model based on the combination of optical scattering model and computer graphics lighting rendering model. Based on the new haze model and the images obtained by the cameras, we can reconstruct the three-dimensional space, accurately calculate the objects and haze in the space, and use the transparency relationship of haze to perform accurate haze removal. To o…
A Generative Data Framework with Authentic Supervision for Underwater Image Restoration and Enhancement
Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究院（TeleAI），中国电信）；Department of Automation, Shanghai Jiao Tong University（自动化系，上海交通大学）；School of Artificial Intelligence, Optics and ElectroNics (iOPEN), Northwestern Polytechnical University（人工智能、光学与电子学学院（iOPEN），西北工业大学）；Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences（西安光学精密机械研究所，中国科学院）
Tel Aviv University（特拉维夫大学）；University of Haifa（海法大学）
Underwater images suffer from color distortion and low contrast, because light is attenuated while it propagates through water. Attenuation under water varies with wavelength, unlike terrestrial images where attenuation is assumed to be spectrally uniform. The attenuation depends both on the water body and the 3D structure of the scene, making color restoration difficult. Unlike existing single underwater image enhancement techniques, our method takes into account multiple spectral profiles of different water types. By estimating just two additional global parameters: the attenuation ratios of…
Huazhong Institute of Electro-Optics（华中光电技术研究所）；Wuhan National Laboratory for Optoelectronics（武汉光电国家实验室）；Harbin Engineering University（哈尔滨工程大学）
In real-world underwater environment, exploration of seabed resources, underwater archaeology, and underwater fishing rely on a variety of sensors, vision sensor is the most important one due to its high information content, non-intrusive, and passive nature. However, wavelength-dependent light attenuation and back-scattering result in color distortion and haze effect, which degrade the visibility of images. To address this problem, firstly, we proposed an unsupervised generative adversarial network (GAN) for generating realistic underwater images (color distortion and haze effect) from in-air…
Jiangxi Normal University（江西师范大学）；East China Normal University（华东师范大学）
Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed. Most of them follow a classic atmospheric scattering model which is an elegant simplified physical model based on the assumption of single-scattering and homogeneous atmospheric medium. The formulation of haze in realistic environment is more complicated. In this paper, we propose to take its essential mechanism as "black box", and focus on learning an input-adaptive trainable end-to-end dehazing model. An U-Net like encoder-decoder deep network via progressi…
Jimei University（集美大学）；Southwest University（西南大学）；China University of Petroleum（中国石油大学）
Removing adverse weather conditions like rain, fog, and snow from images is a challenging problem. Although the current recovery algorithms targeting a specific condition have made impressive progress, it is not flexible enough to deal with various degradation types. We propose an efficient and compact image restoration network named DAN-Net (Degradation-Adaptive Neural Network) to address this problem, which consists of multiple compact expert networks with one adaptive gated neural. A single expert network efficiently addresses specific degradation in nasty winter scenes relying on the compa…
UniUIR: Considering Underwater Image Restoration as An All-in-One Learner
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程研究中心，计算机学院，武汉大学）；School of Information Engineering, Guangdong University of Technology（信息工程学院，广东工业大学）；Horizon Robotics
Severe color casts, low contrast and blurriness of underwater images caused by light absorption and scattering result in a difficult task for exploring underwater environments. Different from most of previous underwater image enhancement methods that compute light attenuation along object-camera path through hazy image formation model, we propose a novel jointly wavelength compensation and dehazing network (JWCDN) that takes into account the wavelength attenuation along surface-object path and the scattering along object-camera path simultaneously. By embedding a simplified underwater formatio…
National Chung Hsing University（中兴大学）；National Taichung University of Science and Technology（台中科技大学）
Image restoration is a challenging and ill-posed problem which also has been a long-standing issue. However, most of learning based restoration methods are proposed to target one degradation type which means they are lack of generalization. In this paper, we proposed a multi-branch restoration model inspired from the Human Visual System (i.e., Retinal Ganglion Cells) which can achieve multiple restoration tasks in a general framework. The experiments show that the proposed multi-branch architecture, called CMFNet, has competitive performance results on four datasets, including image dehazing, …
Dual High-Order Total Variation Model for Underwater Image Restoration
College of Computer Science and Technology, Qingdao University（青岛大学计算机科学与技术学院）；Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, the School of Automation and Electrical Engineering, University of Science and Technology Beijing（北京科技大学自动化与电气工程学院 工业过程知识自动化教育部重点实验室）
Images of static scenes submerged beneath a wavy water surface exhibit severe non-rigid distortions. The physics of water flow suggests that water surfaces possess spatio-temporal smoothness and temporal periodicity. Hence they possess a sparse representation in the 3D discrete Fourier (DFT) basis. Motivated by this, we pose the task of restoration of such video sequences as a compressed sensing (CS) problem. We begin by tracking a few salient feature points across the frames of a video sequence of the submerged scene. Using these point trajectories, we show that the motion fields at all other…
Centre for Intelligent Sensing, Queen Mary University of London（伦敦玛丽女王大学智能感知中心）
Underwater images are degraded by the selective attenuation of light that distorts colours and reduces contrast. The degradation extent depends on the water type, the distance between an object and the camera, and the depth under the water surface the object is at. Underwater image filtering aims to restore or to enhance the appearance of objects captured in an underwater image. Restoration methods compensate for the actual degradation, whereas enhancement methods improve either the perceived image quality or the performance of computer vision algorithms. The growing interest in underwater ima…
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…
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Hong Kong University of Science and Technology（香港科技大学）；Rakuten Institute of Technology（乐天理工研究院）；Imperial College London（伦敦帝国学院）；KAIST（韩国科学技术院）；Tencent（腾讯）；Australian National University（澳大利亚国立大学）；Nanjing University（南京大学）
The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an important role since it allows to increase the quality of the images, without the need for more expensive equipment. This is a critical example of the important role of the machine learning algorithms to support marine exploration and monitoring, reducing the need for human intervention like the manual processing of the images, thus saving time, effort, and cost. This paper is the first …
Haze-Aware Attention Network for Single-Image Dehazing
School of Ocean Information Engineering, Jimei University（集美大学海洋信息工程学院）；College of Artificial Intelligence, Southwest University（西南大学人工智能学院）；School of Computer Science, Jimei University（集美大学计算机科学学院）
The appearance of objects in underwater images is degraded by the selective attenuation of light, which reduces contrast and causes a colour cast. This degradation depends on the water environment, and increases with depth and with the distance of the object from the camera. Despite an increasing volume of works in underwater image enhancement and restoration, the lack of a commonly accepted evaluation measure is hindering the progress as it is difficult to compare methods. In this paper, we review commonly used colour accuracy measures, such as colour reproduction error and CIEDE2000, and no-…
Laplace-Mamba: Laplace Frequency Prior-Guided Mamba-CNN Fusion Network for Image Dehazing
School of Computer Science and Technology, Anhui University of Technology（安徽理工大学计算机科学与技术学院）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；College of Artificial Intelligence, Taiyuan University of Technology（太原科技大学人工智能学院）
UBTECH Sydney Artificial Intelligence Centre（优必选悉尼人工智能中心）；University of Sydney（悉尼大学）
Single image dehazing is a critical image pre-processing step for subsequent high-level computer vision tasks. However, it remains challenging due to its ill-posed nature. Existing dehazing models tend to suffer from model overcomplexity and computational inefficiency or have limited representation capacity. To tackle these challenges, here we propose a fast and accurate multi-scale end-to-end dehazing network called FAMED-Net, which comprises encoders at three scales and a fusion module to efficiently and directly learn the haze-free image. Each encoder consists of cascaded and densely connec…
