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 low-frequency component is enhanced by using an adaptive color and contrast enhancement (ACCE) strategy. The proposed ACCE is an adaptive variational framework implemented in the HSI color space, which integrates data term and regularized term, as well as introduces Gaussian weight and Heaviside function to avoid over-enhancement and oversaturation. Moreover, we derive a numerical solution for ACCE, and adopt a pyramid-based strategy to accelerate the solving procedure. Experimental results demonstrate that our strategy is effective in color correction, visibility improvement, and detail revealing. Comparison with state-of-the-art techniques also validate the superiority of proposed method. Furthermore, we have verified the utility of our proposed ACCE-D for enhancing other types of degraded scenes, including foggy scene, sandstorm scene and low-light scene.
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）
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）
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…
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））
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 …
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…
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, …
Image-Conditional Diffusion Transformer for Underwater Image Enhancement
Ocean College, Jiangsu University of Science and Technology（江苏科技大学海洋学院）；Nanjing Research Institute of Electronic Equipment, China Aerospace Science and Industry Corporation（中国航天科工集团有限公司南京电子设备研究所）；Ocean College, Zhejiang University（浙江大学海洋学院）
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…
U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising Training
National Key Laboratory of Autonomous Marine Vehicle Technology, Harbin Engineering University（自主水下机动车辆技术国家重点实验室，哈尔滨工程大学）；Hong Kong Polytechnic University（香港理工大学）
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）
Recent studies on learning-based image denoising have achieved promising performance on various noise reduction tasks. Most of these deep denoisers are trained either under the supervision of clean references, or unsupervised on synthetic noise. The assumption with the synthetic noise leads to poor generalization when facing real photographs. To address this issue, we propose a novel deep image-denoising method by regarding the noise reduction task as a special case of the noise transference task. Learning noise transference enables the network to acquire the denoising ability by observing the…
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）
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）
Institute for Computer Science and Control, Hungarian Academy of Sciences（匈牙利科学院计算机科学与控制研究所）
In this paper, we propose a novel image denoising algorithm using collaborative support-agnostic sparse reconstruction. An observed image is first divided into patches. Similarly structured patches are grouped together to be utilized for collaborative processing. In the proposed collaborative schemes, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the same group. This provides very good patch support estimation, hence enha…
PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment
Chongqing Research Institute of Harbin Institute of Technology（哈尔滨工业大学重庆研究所）；Harbin Institute of Technology（哈尔滨工业大学）；Faculty of Computing（计算机学院）；School of Computer Science（计算机科学学院）；University of Macau（澳门大学）；Institute for Infocomm Research（信息与通信研究所以）
Fraunhofer Institute for Computer Graphics Research (IGD)（弗劳恩霍夫计算机图形研究所）；University of Rostock（罗斯托克大学）
Visual inspection of underwater structures by vehicles, e.g. remotely operated vehicles (ROVs), plays an important role in scientific, military, and commercial sectors. However, the automatic extraction of information using software tools is hindered by the characteristics of water which degrade the quality of captured videos. As a contribution for restoring the color of underwater images, Underwater Denoising Autoencoder (UDAE) model is developed using a denoising autoencoder with U-Net architecture. The proposed network takes into consideration the accuracy and the computation cost to enable…
Riverbed litter monitoring using consumer-grade aerial-aquatic speedy scanner (AASS) and deep learning based super-resolution reconstruction and detection network
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州总医院与哈佛医学院戈登医学影像中心）
Image denoising techniques are essential to reducing noise levels and enhancing diagnosis reliability in low-dose computed tomography (CT). Machine learning based denoising methods have shown great potential in removing the complex and spatial-variant noises in CT images. However, some residue artifacts would appear in the denoised image due to complexity of noises. A cascaded training network was proposed in this work, where the trained CNN was applied on the training dataset to initiate new trainings and remove artifacts induced by denoising. A cascades of convolutional neural networks (CNN)…
We introduce an integrodifferential extension of the edge-enhancing anisotropic diffusion model for image denoising. By accumulating weighted structural information on multiple scales, our model is the first to create anisotropy through multiscale integration. It follows the philosophy of combining the advantages of model-based and data-driven approaches within compact, insightful, and mathematically well-founded models with improved performance. We explore trained results of scale-adaptive weighting and contrast parameters to obtain an explicit modelling by smooth functions. This leads to a t…
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）
The underwater images usually suffers from non-uniform lighting, low contrast, blur and diminished colors. In this paper, we proposed an image based preprocessing technique to enhance the quality of the underwater images. The proposed technique comprises a combination of four filters such as homomorphic filtering, wavelet denoising, bilateral filter and contrast equalization. These filters are applied sequentially on degraded underwater images. The literature survey reveals that image based preprocessing algorithms uses standard filter techniques with various combinations. For smoothing the im…
In this paper, we propose a novel technique to reconstruct 3D surface of an underwater object using stereo images. Reconstructing the 3D surface of an underwater object is really a challenging task due to degraded quality of underwater images. There are various reason of quality degradation of underwater images i.e., non-uniform illumination of light on the surface of objects, scattering and absorption effects. Floating particles present in underwater produces Gaussian noise on the captured underwater images which degrades the quality of images. The degraded underwater images are preprocessed …
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…
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）
Rethinking Conditional Generation for Underwater Salient Object Detection
School of Computer Science and Technology, Hainan University（海南大学计算机科学与技术学院）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education, Shandong University（山东大学机器智能与系统控制教育部重点实验室）；School of Data Science, Lingnan University（岭南大学数据科学学院）
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）
