In low-visibility marine environments characterized by turbidity and darkness, acoustic cameras serve as visual sensors capable of generating high-resolution 2D sonar images. However, acoustic camera images are interfered with by complex noise and are difficult to be directly ingested by downstream visual algorithms. This paper introduces a novel strategy for denoising acoustic camera images using deep learning techniques, which comprises two principal components: a self-supervised denoising framework and a fine feature-guided block. Additionally, the study explores the relationship between the level of image denoising and the improvement in feature-matching performance. Experimental results show that the proposed denoising strategy can effectively filter acoustic camera images without prior knowledge of the noise model. The denoising process is nearly end-to-end without complex parameter tuning and post-processing. It successfully removes noise while preserving fine feature details, thereby enhancing the performance of local feature matching.
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…
Underwater automatic target recognition (UATR) has been a challenging research topic in ocean engineering. Although deep learning brings opportunities for target recognition on land and in the air, underwater target recognition techniques based on deep learning have lagged due to sensor performance and the size of trainable data. This letter proposed a framework for learning the visual representation of underwater acoustic imageries, which takes a transformer-based style transfer model as the main body. It could replace the low-level texture features of optical images with the visual features …
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 …
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…
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）
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…
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）
Subsea images measured by the side scan sonars (SSSs) are necessary visual data in the process of deep-sea exploration by using the autonomous underwater vehicles (AUVs). They could vividly reflect the topography of the seabed, but usually accompanied by complex and severe noise. This paper proposes a deep denoising method for SSS images without high-quality reference data, which uses one single noise SSS image to perform self-supervised denoising. Compared with the classical artificially designed filters, the deep denoising method shows obvious advantages. The denoising experiments are perfor…
TDiR: Transformer based Diffusion for Image Restoration Tasks
Department of Computer Vision（计算机视觉系）；FutureDataMinds；Electrical Engineering Dept（电气工程系）；King Fahd University of Petroleum and Minerals（国王法赫德石油和矿产大学）；Department of Computer Science and Software Engineering（计算机科学与软件工程系）；University of Western Australia（西澳大学）；School of Computing（计算学院）
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 …
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）
SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection
College of Computer and Information Technology, China Three Gorges University（三峡大学计算机与信息学院）；Wuhan University of Technology（武汉理工大学）；Peking University（北京大学）
Can Optical Denoising Clean Sonar Images? A Benchmark and Fusion Approach
Hangzhou Institute of Technology, Xidian University（杭州理工大学）；Department of Engineering, Shenzhen MSU-BIT University（深圳MSU-BIT大学工程系）；School of Information Engineering, Jiangxi University of Science（江西理工大学信息工程学院）；Institute of Deep-sea Science（深海科学研究院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；College of Cyber Security, Jinan University（广州大学网络安全学院）
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）；University of Groningen（格罗宁根大学）
Self-supervised learning has proved to be a powerful approach to learn image representations without the need of large labeled datasets. For underwater robotics, it is of great interest to design computer vision algorithms to improve perception capabilities such as sonar image classification. Due to the confidential nature of sonar imaging and the difficulty to interpret sonar images, it is challenging to create public large labeled sonar datasets to train supervised learning algorithms. In this work, we investigate the potential of three self-supervised learning methods (RotNet, Denoising Aut…
