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An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

Authors: Yan Wang, Wei Song, Giancarlo Fortino, Li-Zhe Qi, Wenqiang Zhang, Antonio Liotta

Published in IEEE Xplore 30 July 2019 View in IEEE Xplore

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. First, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of the state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analyses (the used code is freely available at  https :// github . com / wangyanckxx / Single – Underwater – Image – Enhancement – and – Color – Restoration ). Starting from this paper, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.

View this article on IEEE Xplore

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Recent progress in digital image restoration techniques: : A review

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Information & Contributors

Bibliometrics & citations, view options, recommendations, image retrieval using digital image inpainting techniques.

Image retrieval is an inverse problem in digital image processing. In this paper, the authors deal with restoration of image using digitally image inpainting methods. In this inpainting technique, one can extract a missing an important part or can ...

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• Digital image
• Image restoration
• Degradation
• Transformation
• Review-article

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Preserving Artistic Heritage: A Comprehensive Review of Virtual Restoration Methods for Damaged Artworks

• Review article
• Published: 05 September 2024

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• Praveen Kumar 1 &
• Varun Gupta   ORCID: orcid.org/0000-0002-2633-5920 1  

Restoration of damaged artwork is an important task to preserve the culture and history of humankind. Restoration of damaged artwork is a delicate, complex, and irreversible process that requires preserving the artist’s style and semantics while removing the damages from the artwork. Digital restoration of artworks can guide artists in physically restoring artworks. This paper groups the virtual artwork restoration methods into various categories: image processing, machine learning, encoder-decoder neural networks, and generative adversarial network-based methods. This paper discusses and analyses different restoration methods’ underlying merits and demerits. The category-wise review of various artwork restoration methods reveals that the generative adversarial network-based methods have attracted the attention of researchers in recent years for restoring damaged artworks. This paper describes datasets used for training and testing of artwork restoration methods and discusses various metrics used for performance evaluation of the artwork restoration methods. This paper compares the restoration results of various methods quantitatively using performance evaluation metrics and qualitatively using visual inspection of the results. Further, the paper also identifies research gaps, challenges, and future directions for research in this field. The proposed review aims to provide researchers with an important reference for working in the artwork restoration field.

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Kumar, P., Gupta, V. Preserving Artistic Heritage: A Comprehensive Review of Virtual Restoration Methods for Damaged Artworks. Arch Computat Methods Eng (2024). https://doi.org/10.1007/s11831-024-10175-7

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Underwater image enhancement based on luminance reconstruction by multi-resolution fusion of rgb channels.

1. Introduction

2. related work, 2.1. physical model-based methods, 2.2. non-physical model-based methods, 2.3. deep learning-based methods, 3. proposed algorithm, 3.1. decomposition of image color space, 3.2. multi-resolution fusion-based luminance reconstruction, 3.3. color correction, 4. results and discussion, 4.1. luminance reconstruction evaluation, 4.2. qualitative evaluation, 4.3. quantitative evaluation, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Wang, Y.; Chen, Z.; Yan, G.; Zhang, J.; Hu, B. Underwater Image Enhancement Based on Luminance Reconstruction by Multi-Resolution Fusion of RGB Channels. Sensors 2024 , 24 , 5776. https://doi.org/10.3390/s24175776

Wang Y, Chen Z, Yan G, Zhang J, Hu B. Underwater Image Enhancement Based on Luminance Reconstruction by Multi-Resolution Fusion of RGB Channels. Sensors . 2024; 24(17):5776. https://doi.org/10.3390/s24175776

Wang, Yi, Zhihua Chen, Guoxu Yan, Jiarui Zhang, and Bo Hu. 2024. "Underwater Image Enhancement Based on Luminance Reconstruction by Multi-Resolution Fusion of RGB Channels" Sensors 24, no. 17: 5776. https://doi.org/10.3390/s24175776

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Title: awracle: all-weather image restoration using visual in-context learning.

Abstract: All-Weather Image Restoration (AWIR) under adverse weather conditions is a challenging task due to the presence of different types of degradations. Prior research in this domain relies on extensive training data but lacks the utilization of additional contextual information for restoration guidance. Consequently, the performance of existing methods is limited by the degradation cues that are learnt from individual training samples. Recent advancements in visual in-context learning have introduced generalist models that are capable of addressing multiple computer vision tasks simultaneously by using the information present in the provided context as a prior. In this paper, we propose All-Weather Image Restoration using Visual In-Context Learning (AWRaCLe), a novel approach for AWIR that innovatively utilizes degradation-specific visual context information to steer the image restoration process. To achieve this, AWRaCLe incorporates Degradation Context Extraction (DCE) and Context Fusion (CF) to seamlessly integrate degradation-specific features from the context into an image restoration network. The proposed DCE and CF blocks leverage CLIP features and incorporate attention mechanisms to adeptly learn and fuse contextual information. These blocks are specifically designed for visual in-context learning under all-weather conditions and are crucial for effective context utilization. Through extensive experiments, we demonstrate the effectiveness of AWRaCLe for all-weather restoration and show that our method advances the state-of-the-art in AWIR.

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