Blind Image Deblurring Using a Non-Linear Channel Prior Based on Dark and Bright Channels

Ge, Xianyu; Tan, Jieqing; Zhang, Li

doi:10.1109/tip.2021.3101154

Cited by 13 publications

(15 citation statements)

References 55 publications

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“…com/project/AID/, accessed on 26 November 2021). The following shows the comparison results of our method with four algorithms, which use the heavy-tailed prior (HTP) [29], the dark channel prior (Dark) [14], L 0 -regularized intensity and gradient prior (L 0 ) [24] and the non-linear channel prior (NLCP) [16].…”

Section: Experiments Resultsmentioning

confidence: 99%

“…Although the image deblurring algorithm based on LBP prior can effectively obtain a clear image in theory, it needs to be quantitatively evaluated and verified in the actual situation. As shown in Figure 17, the proposed algorithm is compared with HTP [29], Dark [14], L 0 [24] and NLCP [16]. The performance of our algorithm in error accumulation rate is second only to MAP, and it is better than other methods in the two indicators of average PSNR and average SSIM.…”

Section: Effectiveness Of the Lbp Priormentioning

confidence: 97%

“…Yan et al [15] combined dark channel and bright channel and designed an extreme channel prior algorithm. Since then, Yang [32] and Ge [16] have made further improvements to the problems faced by the extreme channel prior algorithms. At the same time, the blind image deblurring algorithms, based on local prior information, have also made significant achievements, i.e., the method based on the local maximum gradient (LMG) prior proposed by Chen et al [25] and the method based on the local maximum difference (LMD) prior proposed by Liu et al [33].…”

Section: Image Priors-based Algorithmsmentioning

confidence: 99%

“…Referring to a variety of restoration algorithms [14][15][16]24,25,38], we replace the image intensities in (10) with the image gradients to make the estimated blur kernel more accurate. The formula for solving the blur kernel is: min…”

Section: Estimating the Blur Kernelmentioning

confidence: 99%

“…To avoid the algorithm failure caused by insufficient dark channel pixels in the image, Yan et al [15] introduced both dark channels and bright channels into the image-blind deblurring model. Ge et al [16] pointed out that the above two methods will fail when there are insufficient extreme pixels. Therefore, they constructed a non-linear channel (NLC) prior and introduced it into a blind images deblurring algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Blind Remote Sensing Image Deblurring Using Local Binary Pattern Prior

et al. 2022

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In this paper, an algorithm based on local binary pattern (LBP) is proposed to obtain clear remote sensing images under the premise of unknown causes of blurring. We find that LBP can completely record the texture features of the images, which will not change widely due to the generation of blur. Therefore, LBP prior is proposed, which can filter out the pixels containing important textures in the blurry image through the mapping relationship. The corresponding processing methods are adopted for different types of pixels to cope with the challenges brought by the rich texture and details of remote sensing images and prevent over-sharpening. However, the existence of LBP prior increases the difficulty of solving the model. To solve the model, we construct the projected alternating minimization (PAM) algorithm that involves the construction of the mapping matrix, the fast iterative shrinkage-thresholding algorithm (FISTA) and the half-quadratic splitting method. Experiments with the AID dataset show that the proposed method can achieve highly competitive processing results for remote sensing images.

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