Image Deblurring via Extreme Channels Prior

By Yanyang Yan, Wenqi Ren, Yuanfang Guo, Rui Wang, Xiaochun Cao. Computer Vision and Pattern Recognition, 2017

358 citations recorded by the source corpus.

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ALL Snow Removed: Single Image Desnowing Algorithm Using Hierarchical Dual-tree Complex Wavelet Representation and Contradict Channel Loss (2021, background): “Compared with existing image priors such as the dark channel prior (DCP) [31] and the bright channel prior (BCP) [32], the CC can be more discriminative in the snow scenario.”
Adversarial Spatio-Temporal Learning for Video Deblurring (2018, background): “Modern single-image deblur-ring methods iteratively estimate uniform or non-uniform blur kernels and the latent sharp image given a single blurry image [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20].”
Neural Blind Deconvolution Using Deep Priors (2019, methodology): “Under the MAP-based framework, many fixed and handcrafted regularization terms have been presented for latent clean image and blur kernel [22, 24, 29, 29, 34, 41, 50, 56].”
Bringing a Blurry Frame Alive at High Frame-Rate With an Event Camera (2018, background): “Non-gradientbased priors such as the color line based prior [16], and the extreme channel (dark/bright channel) prior [22, 38] have also been explored.”
Learning to Super-Resolve Blurry Face and Text Images (2017, methodology): “Several recent methods introduce new image priors that favor clear images over blurred ones in the MAP framework [20, 45, 30, 33, 46].”