Two-Stage Underwater Image Restoration Algorithm Based on Physical Model and Causal Intervention

Hao, Junyu; Yang, Hongbo; Hou, Xia; Zhang, Yang

doi:10.1109/lsp.2022.3232035

Cited by 12 publications

(4 citation statements)

References 23 publications

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“…To avoid the ill-posed problem, the forward scattering with little effect is removed. In conclusion, the simplified version of Jaffe-McGlamery model is given by [58,59]:…”

Section: Methodology a Underwater Imaging Model With Noisementioning

confidence: 99%

Single Underwater Image Restoration Using Variational Framework Guided by Imaging Model With Noise

Dai,

Lin

2024

IEEE Access

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Underwater images typically present poor visibility, color distortion, and noise, which limit the application in several high-level tasks of image analysis. To address these corruptions, a novel method is proposed to reconstruct high-quality underwater images, which is designed by integrating imaging model with noise and variational framework. Specifically, an improved underwater imaging model is first introduced by separating noise from real underwater scene. Subsequently, the hazy curves of degraded colors are decomposed to estimate transmission map, and a color loss prior is employed to correct the transmission map. Moreover, a first-order gradient guided filter is proposed to refine the transmission map. An evaluation formula is designed by combining illumination, contrast, and color deviation priors to accurately search for the background region. Finally, a variational model is established to restore underwater images and suppress noise based on the improved imaging model and image priors. Experimental results validate that the proposed method surpasses several outstanding approaches, demonstrating its well effectiveness in improving contrast, correcting color, and suppressing noise.INDEX TERMS Underwater image restoration, variational framework, imaging model with noise.

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“…To avoid the ill-posed problem, the forward scattering with little effect is removed. In conclusion, the simplified version of Jaffe-McGlamery model is given by [58,59]:…”

Section: Methodology a Underwater Imaging Model With Noisementioning

confidence: 99%

Single Underwater Image Restoration Using Variational Framework Guided by Imaging Model With Noise

Dai,

Lin

2024

IEEE Access

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“…In a different area of research, people are looking into how to combine traditional picture processing methods with deep learning methods. For example, [12] created a deep convolutional neural network that works with total variation regularization to fix images and separate them into groups. This project combined the best features of deep learning for feature learning with the regularization skills of old-fashioned methods.…”

Section: Related Workmentioning

confidence: 99%

Application of Mathematical Modelling and Deep Learning in Image Restoration using Edge Preservation Method

Bhumika Neole

2024

cana

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The image restoration has witnessed significant advancements with the integration of deep learning techniques with offering unparalleled capabilities in enhancing image quality. This paper study proposed a novel approach to image restoration by incorporating a sophisticated edge preservation method using the deep learning framework. The method aims to address the challenge of preserving high-frequency details, such as edges, while restoring images from various forms of degradation. Also we investigate the use of deep neural networks trained on a different noisy image to restore a clean image with preserving edges of original image. The Deep convolutional neural network (DCNNs), and ResNet50 in deep learning model learns elaborate patterns and features, enabling the reconstruction of images with improved clarity and fidelity. The proposed Deep Convolution Neural Network and ResNet50 methods are designed to restore image content with intelligently preserve and enhance edge information, crucial for maintaining the structural integrity of the original scene. The proposed model is trained on diverse datasets, encompassing a wide range of image degradations, ensuring robust performance across various real-world scenarios. Experimental results demonstrate the efficacy of the proposed approach in comparison to existing methods, showcasing superior edge preservation and overall restoration quality. This research contributes to the advancement of image processing techniques, offering a powerful tool for applications such as medical imaging, satellite imagery, and digital photography, where maintaining fine details is essential for accurate interpretation and analysis.

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“…The network, leveraging multiple color space embeddings and blending physical and learningbased methods, significantly enhances underwater image visual quality. The authors of [76] delineated the statistical relationship between underwater and restored images based on the scattering model, introducing a novel, efficient underwater image restoration (UIR) model. This model addresses the UIR challenge by segmenting it into global restoration and local compensation, developing dedicated modules for each.…”

Section: Underwater Optical Image Quality Improvementmentioning

confidence: 99%

Breakthrough Underwater Physical Environment Limitations on Optical Information Representations: An Overview and Suggestions

Li,

Zhang,

Zhang

et al. 2024

JMSE

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Underwater optics have seen a notable surge of interest in recent years, emerging as a critical medium for conveying information crucial to underwater resource exploration, autonomous underwater vehicle navigation, etc. The intricate dynamics of underwater optical transmission, influenced by factors such as the absorption by the water and scattering by multiple particles, present considerable challenges. One of the most critical issues is that the optical information representation methods fail to take into account the impact of the underwater physical environment. We conducted a comprehensive review and analysis of recent advancements in underwater optical transmission laws and models. We summarized and analyzed relevant research on the effects of underwater particles and turbulence on light and analyzed the polarization effects in various environments. Then, the roles of various types of underwater optical propagation models were analyzed. Although optical models in complex environments are still mostly based on Monte Carlo methods, many underwater optical propagation mechanisms have been revealed and can promote the impacts of optical information expression. We delved into the cutting-edge research findings across three key domains: the enhancement of underwater optical image quality, the 3D reconstruction from monocular images, and the underwater wireless optical communication, examining the pivotal role played by light transmission laws and models in these areas. Drawing upon our extensive experience in underwater optics, including underwater optical sensor development and experiments, we identified and underscored future directions in this field. We advocate for the necessity of further advancements in the comprehension of underwater optical laws and physical models, emphasizing the importance of their expanded application in underwater optical information representations. Deeper exploration into these areas is not only warranted but essential for pushing the boundaries of current underwater optical technologies and unlocking new potential for their application in underwater optical sensor developments, underwater exploration, environmental monitoring, and beyond.

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Two-Stage Underwater Image Restoration Algorithm Based on Physical Model and Causal Intervention

Cited by 12 publications

References 23 publications

Single Underwater Image Restoration Using Variational Framework Guided by Imaging Model With Noise

Single Underwater Image Restoration Using Variational Framework Guided by Imaging Model With Noise

Application of Mathematical Modelling and Deep Learning in Image Restoration using Edge Preservation Method

Breakthrough Underwater Physical Environment Limitations on Optical Information Representations: An Overview and Suggestions

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