Proceedings ArticleDOI
Image Deblurring via Extreme Channels Prior
Yanyang Yan,Wenqi Ren,Yuanfang Guo,Rui Wang,Xiaochun Cao +4 more
- pp 6978-6986
TLDR
This work observes that the bright pixels in the clear images are not likely to be bright after the blur process, and proposes a technique fordeblurring such images which elevates the performance of existing motion deblurring algorithms and takes advantage of both Bright and Dark Channel Prior.Abstract:
Camera motion introduces motion blur, affecting many computer vision tasks. Dark Channel Prior (DCP) helps the blind deblurring on scenes including natural, face, text, and low-illumination images. However, it has limitations and is less likely to support the kernel estimation while bright pixels dominate the input image. We observe that the bright pixels in the clear images are not likely to be bright after the blur process. Based on this observation, we first illustrate this phenomenon mathematically and define it as the Bright Channel Prior (BCP). Then, we propose a technique for deblurring such images which elevates the performance of existing motion deblurring algorithms. The proposed method takes advantage of both Bright and Dark Channel Prior. This joint prior is named as extreme channels prior and is crucial for achieving efficient restorations by leveraging both the bright and dark information. Extensive experimental results demonstrate that the proposed method is more robust and performs favorably against the state-of-the-art image deblurring methods on both synthesized and natural images.read more
Citations
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Journal ArticleDOI
Image deblurring based on enhanced salient edge selection
TL;DR: Zhang et al. as discussed by the authors proposed an enhanced salient edge selection for blind image deblurring, where the image sharpening operator was adopted to guide the finer image structure when salient edges provided strong edge information for blur kernel estimation.
Journal ArticleDOI
Patch-Wise Blind Image Deblurring via Michelson Channel Prior
TL;DR: A novel image channel is proposed in combination with dark channel and bright channel in this paper to consider the effects of the all types of pixels, namely, Michelson channel pixels and outperforms the existing art-of-the-state of unsupervised image deblurring methods on both synthesized and natural images.
Posted Content
The Maximum Entropy on the Mean Method for Image Deblurring
TL;DR: In this paper, the authors proposed an alternative approach, shifting the paradigm towards regularization at the level of the probability distribution on the space of images, based on the idea of maximum entropy on the mean.
Proceedings ArticleDOI
A robust non-blind deblurring method using deep denoiser prior
TL;DR: Zhang et al. as mentioned in this paper proposed a kernel error term to rectify the given kernel in the midst of the deconvolution process, and a residual error term is also introduced to deal with the outliers caused by noise or saturation.
Journal ArticleDOI
Blind Image Deblurring with Unknown Kernel Size and Substantial Noise
TL;DR: Recently, Sun et al. as mentioned in this paper proposed a single-instance deblurring method that is stable against unknown kernel size and substantial noise, which is the first of its kind in the literature.
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