Non-uniform illumination underwater image restoration via illumination channel sparsity prior
Underwater image quality is seriously degraded due to the insufficient light in water. Although artificial illumination can assist imaging, it often brings non-uniform illumination phenomenon. To this end, we develop an illumination channel sparsity prior (ICSP) guided variational framework for non-...
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sg-ntu-dr.10356-1718242023-11-09T02:44:13Z Non-uniform illumination underwater image restoration via illumination channel sparsity prior Hou, Guojia Li, Nan Zhuang, Peixian Li, Kunqian Sun, Haihan Li, Chongyi School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Non-uniform Illumination Underwater Image Restoration Underwater image quality is seriously degraded due to the insufficient light in water. Although artificial illumination can assist imaging, it often brings non-uniform illumination phenomenon. To this end, we develop an illumination channel sparsity prior (ICSP) guided variational framework for non-uniform Illumination underwater image restoration. Technically, the illumination channel sparsity prior is built on the observation that the illumination channel of a uniform-light underwater image in HSI color space contains few pixels whose intensity is very low. Then according to the Retinex theory, we design a variational model with L<sub>0</sub> norm term, constraint term, and gradient term, by integrating the proposed ICSP into an extended underwater image formation model. Such three regularizations are effective in enhancing the brightness, correcting color distortion, and revealing structures and fine-scale details. Meanwhile, we exploit a fast numerical algorithm on the base of the alternating direction method of multipliers (ADMM) to accelerate solving this optimization problem. We also collect a benchmark dataset, namely NUID that contains 925 real underwater images of different non-uniform illumination. Extensive experiments demonstrate that our proposed method is effective in terms of quantitative and qualitative comparisons, ablation studies, convergence analysis, and applications. The code and dataset are available at https://github.com/Hou-Guojia/ICSP. This work was supported in part by the National Natural Science Foundation of China under Grant 61901240, Grant 62171252, and Grant 61701245, in part by the Natural Science Foundation of Shandong Province, China, under Grant ZR2019BF042, in part by the Laoshan Laboratory under Grant LSKJ202204302, in part by the Shandong Provincial Education Department, China, and in part by the China Postdoctoral Science Foundation under Grant 2017M612204, and Grant 2021M701903. 2023-11-09T02:44:13Z 2023-11-09T02:44:13Z 2023 Journal Article Hou, G., Li, N., Zhuang, P., Li, K., Sun, H. & Li, C. (2023). Non-uniform illumination underwater image restoration via illumination channel sparsity prior. IEEE Transactions On Circuits and Systems for Video Technology. https://dx.doi.org/10.1109/TCSVT.2023.3290363 1051-8215 https://hdl.handle.net/10356/171824 10.1109/TCSVT.2023.3290363 2-s2.0-85163566848 en IEEE Transactions on Circuits and Systems for Video Technology © 2023 IEEE. All rights reserved. |
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Engineering::Mechanical engineering Non-uniform Illumination Underwater Image Restoration Hou, Guojia Li, Nan Zhuang, Peixian Li, Kunqian Sun, Haihan Li, Chongyi Non-uniform illumination underwater image restoration via illumination channel sparsity prior |
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Underwater image quality is seriously degraded due to the insufficient light in water. Although artificial illumination can assist imaging, it often brings non-uniform illumination phenomenon. To this end, we develop an illumination channel sparsity prior (ICSP) guided variational framework for non-uniform Illumination underwater image restoration. Technically, the illumination channel sparsity prior is built on the observation that the illumination channel of a uniform-light underwater image in HSI color space contains few pixels whose intensity is very low. Then according to the Retinex theory, we design a variational model with L<sub>0</sub> norm term, constraint term, and gradient term, by integrating the proposed ICSP into an extended underwater image formation model. Such three regularizations are effective in enhancing the brightness, correcting color distortion, and revealing structures and fine-scale details. Meanwhile, we exploit a fast numerical algorithm on the base of the alternating direction method of multipliers (ADMM) to accelerate solving this optimization problem. We also collect a benchmark dataset, namely NUID that contains 925 real underwater images of different non-uniform illumination. Extensive experiments demonstrate that our proposed method is effective in terms of quantitative and qualitative comparisons, ablation studies, convergence analysis, and applications. The code and dataset are available at https://github.com/Hou-Guojia/ICSP. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Hou, Guojia Li, Nan Zhuang, Peixian Li, Kunqian Sun, Haihan Li, Chongyi |
format |
Article |
author |
Hou, Guojia Li, Nan Zhuang, Peixian Li, Kunqian Sun, Haihan Li, Chongyi |
author_sort |
Hou, Guojia |
title |
Non-uniform illumination underwater image restoration via illumination channel sparsity prior |
title_short |
Non-uniform illumination underwater image restoration via illumination channel sparsity prior |
title_full |
Non-uniform illumination underwater image restoration via illumination channel sparsity prior |
title_fullStr |
Non-uniform illumination underwater image restoration via illumination channel sparsity prior |
title_full_unstemmed |
Non-uniform illumination underwater image restoration via illumination channel sparsity prior |
title_sort |
non-uniform illumination underwater image restoration via illumination channel sparsity prior |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/171824 |
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1783955590199902208 |