Abstract | ||
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Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels. In this paper, we propose a non-blind deblurring network (NBDN) to restore night blurry images. To mitigate the side effects brought by the pixels that violate the blur model, we develop a confidence estimation unit (CEU) to estimate a map which ensures smaller contributions of these pixels in the deconvolution steps which are optimized by the conjugate gradient (CG) method. Moreover, unlike the existing methods using manually tuned hyper-parameters in their frameworks, we propose a hyper-parameter estimation unit (HPEU) to adaptively estimate hyper-parameters for better image restoration. The experimental results demonstrate that the proposed network performs favorably against state-of-the-art algorithms both quantitatively and qualitatively. |
Year | DOI | Venue |
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2021 | 10.1109/CVPR46437.2021.01040 | 2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021 |
DocType | ISSN | Citations |
Conference | 1063-6919 | 0 |
PageRank | References | Authors |
0.34 | 9 | 6 |
Name | Order | Citations | PageRank |
---|---|---|---|
Liang Chen | 1 | 62 | 9.36 |
Jiawei Zhang | 2 | 111 | 11.52 |
Jin-shan Pan | 3 | 567 | 30.84 |
SongNan Lin | 4 | 0 | 1.35 |
Faming Fang | 5 | 58 | 12.96 |
Jimmy S. J. Ren | 6 | 324 | 23.85 |