Enhancing Underwater Light Field Images via Global Geometry-aware Diffusion Process
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学学院）；School of Mathematics and Statistics and the Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University（西安交通大学数学与统计学学院和教育部智能网络与网络安全重点实验室）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；Macao Institute of Systems Engineering, Macau University of Science and Technology（澳门系统工程研究院，澳门科学大学）
This work studies the challenging problem of acquiring high-quality underwater images via 4-D light field (LF) imaging. To this end, we propose GeoDiff-LF, a novel diffusion-based framework built upon SD-Turbo to enhance underwater 4-D LF imaging by leveraging its spatial-angular structure. GeoDiff-LF consists of three key adaptations: (1) a modified U-Net architecture with convolutional and attention adapters to model geometric cues, (2) a geometry-guided loss function using tensor decomposition and progressive weighting to regularize global structure, and (3) an optimized sampling strategy with noise prediction to improve efficiency. By integrating diffusion priors and LF geometry, GeoDiff-LF effectively mitigates color distortion in underwater scenes. Extensive experiments demonstrate that our framework outperforms existing methods across both visual fidelity and quantitative performance, advancing the state-of-the-art in enhancing underwater imaging. The code will be publicly available at https://github.com/linlos1234/GeoDiff-LF.
Ocean4D: Generative Underwater 4D Reconstruction via Medium-Aware Video Diffusion
Faculty of Information Science and Engineering, Ocean University of China（中国海洋大学信息科学与工程学部）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement
School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）；School of Artificial Intelligence, Nanjing Normal University（人工智能学院，南京师范大学）
Qingdao University（青岛大学）；Ocean University of China（中国海洋大学）
Images captured underwater are often characterized by low contrast, color distortion, and noise. To address these visual degradations, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework for underwater image enhancement. In the proposed framework, Difference of Gaussian (DoG) filter and bilateral filter are respectively employed to decompose the high-frequency and low-frequency components. Benefited from this separation, we utilize soft-thresholding operation to suppress the noise in the high-frequency component. Specially, the…
BARIS: Boundary-Aware Refinement with Environmental Degradation Priors for Robust Underwater Instance Segmentation
Graduate Institute of Communication Engineering, National Taiwan University（台湾大学通信工程研究所）；Department of Electrical Engineering, National Taiwan University（台湾大学电子工程系）
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…
Tree-Mamba: A Tree-Aware Mamba for Underwater Monocular Depth Estimation
Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education（知识自动化工业过程关键实验室，教育部）；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）；School of Ophthalmology and Optometry, Wenzhou Medical University（温州医科大学眼视光学院）；Department of Automation, Tsinghua University（清华大学自动化系）；Deepinfar Ocean Technology Inc.（深海远航技术有限公司）；School of Data Science, Lingnan University（岭南大学数据科学学院）；College of Computer Science, Nankai University（南开大学计算机学院）
Autonomous underwater vehicles (AUVs) rely on a variety of sensors - acoustic, inertial and visual - for intelligent decision making. Due to its non-intrusive, passive nature, and high information content, vision is an attractive sensing modality, particularly at shallower depths. However, factors such as light refraction and absorption, suspended particles in the water, and color distortion affect the quality of visual data, resulting in noisy and distorted images. AUVs that rely on visual sensing thus face difficult challenges, and consequently exhibit poor performance on vision-driven tasks…
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
School of Computer Science and Engineering, Huizhou University（惠州大学计算机科学与工程学院）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）
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）
Spatiotemporal Degradation-Aware 3D Gaussian Splatting for Realistic Underwater Scene Reconstruction
School of Astronautics Beihang University Beijing China（航天学院 北航 北京 中国）；School of Artificial Intelligence Beihang University Beijing China（人工智能学院 北航 北京 中国）；Zhongguancun Academy Beijing China（中关村学院 北京 中国）；Beihang University School of Astronautics Beijing China（北航 航天学院 北京 中国）；Beihang University（北航）；Zhongguancun Academy（中关村学院）
Underwater images suffer from extremely unfavourable conditions. Light is heavily attenuated and scattered. Attenuation creates change in hue, scattering causes so called veiling light. General state of the art methods for enhancing image quality are either unreliable or cannot be easily used in underwater operations. On the other hand there is a well known method for haze removal in air, called Dark Channel Prior. Even though there are known adaptations of this method to underwater applications, they do not always work correctly. This work elaborates and improves upon the initial concept pres…
Enhancing Underwater Images via Adaptive Semantic-aware Codebook Learning
School of Information Science and Engineering, Ocean University of China（信息科学与工程学院，中国海洋大学）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程学院，密苏里州立大学）
High-Resolution Underwater Camouflaged Object Detection: GBU-UCOD Dataset and Topology-Aware and Frequency-Decoupled Networks
College of Shipbuilding Engineering, Harbin Engineering University（哈尔滨工程大学船舶工程学院）；School of Information Science and Technology, Great Bay University（广东工业大学信息科学与技术学院）
WaterFlow: Explicit Physics-Prior Rectified Flow for Underwater Saliency Mask Generation
Hainan University, China（海南大学）；Huazhong University of Science and Technology, China（华中科技大学）；Shenzhen University, China（深圳大学）；Lingnan University, Hong Kong（岭南大学）
Owing to refraction, absorption, and scattering of light by suspended particles in water, raw underwater images suffer from low contrast, blurred details, and color distortion. These characteristics can significantly interfere with the visibility of underwater images and the result of visual tasks, such as segmentation and tracking. To address this problem, we propose a new robust adversarial learning framework via physics model based feedback control and domain adaptation mechanism for enhancing underwater images to get realistic results. A new method for simulating underwater-like training d…
Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method
School of Mathematics and Statistics, Xi’an Jiaotong University（西安交通大学数学与统计学学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University（西安交通大学数学与统计学学院和教育部智能网络与网络安全重点实验室）；Pazhou Laboratory (Huangpu), Guangzhou（广州黄埔实验室（华南））
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））
Downstream Task Inspired Underwater Image Enhancement: A Perception-Aware Study from Dataset Construction to Network Design
State Key Laboratory of Physical Oceanography, Ocean University of China（中国海洋大学物理海洋国家重点实验室）；Department of Electrical and Computer Engineering, Mississippi State University（密苏里州立大学电子与计算机工程系）
Raw underwater images are degraded due to wavelength dependent light attenuation and scattering, limiting their applicability in vision systems. Another factor that makes enhancing underwater images particularly challenging is the diversity of the water types in which they are captured. For example, images captured in deep oceanic waters have a different distribution from those captured in shallow coastal waters. Such diversity makes it hard to train a single model to enhance underwater images. In this work, we propose a novel model which nicely handles the diversity of water during the enhanc…
This study examines the effectiveness of spatio-temporal modeling and the integration of spatial attention mechanisms in deep learning models for underwater object detection. Specifically, in the first phase, the performance of temporal-enhanced YOLOv5 variant T-YOLOv5 is evaluated, in comparison with the standard YOLOv5. For the second phase, an augmented version of T-YOLOv5 is developed, through the addition of a Convolutional Block Attention Module (CBAM). By examining the effectiveness of the already pre-existing YOLOv5 and T-YOLOv5 models and of the newly developed T-YOLOv5 with CBAM. Wit…
AquaGS: Fast Underwater Scene Reconstruction with SfM-Free Gaussian Splatting
School of Electronic Information and Electrical Engineering（电子信息与电气工程学院）；Shanghai Jiao Tong University（上海交通大学）；State Key Laboratory of Ocean Engineering（海洋工程国家重点实验室）
LUIEO: A Lightweight Model for Integrating Underwater Image Enhancement and Object Detection
School of Mathematics, North University of China（中北大学数学学院）；College of Computer and Information Technology, Shanxi University（山西大学计算机与信息技术学院）；School of Mathematics Sciences, Beijing Normal University（北京师范大学数学科学学院）
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）
Universidade Federal de Minas Gerais (UFMG)（米纳斯吉拉斯联邦大学）
The image processing community has witnessed remarkable advances in enhancing and restoring images. Nevertheless, restoring the visual quality of underwater images remains a great challenge. End-to-end frameworks might fail to enhance the visual quality of underwater images since in several scenarios it is not feasible to provide the ground truth of the scene radiance. In this work, we propose a CNN-based approach that does not require ground truth data since it uses a set of image quality metrics to guide the restoration learning process. The experiments showed that our method improved the vi…
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…
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, …
Queensland University of Technology（昆士兰科技大学）；Data61, CSIRO（联邦科学与工业研究组织数据61研究所）
Monitoring coral reefs using underwater vehicles increases the range of marine surveys and availability of historical ecological data by collecting significant quantities of images. Analysis of this imagery can be automated using a model trained to perform semantic segmentation, however it is too costly and time-consuming to densely label images for training supervised models. In this letter, we leverage photo-quadrat imagery labeled by ecologists with sparse point labels. We propose a point label aware method for propagating labels within superpixel regions to obtain augmented ground truth fo…
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…
