Total Variation Blind Deconvolution: The Devil Is in the Details

By Daniel J. Perrone, Paolo Favaro. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

295 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/de712e6134522509f538ac69725da89be1e4f6aa

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Citations in the corpus

Neural Blind Deconvolution Using Deep Priors (2019, methodology): “Perrone and Favaro [32] show that the success of existing methods can be attributed to some optimization details, e.g ., projected alternating minimization and delayed normalization of k .”
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search (2018, background): “…order to resolve or mitigate its ill-posedness, researchers have incorporated image priors such as edge statistics (Fattal, 2007), total variation (Perrone & Favaro, 2014), and sparse representation (Aharon et al., 2006; Yang et al., 2010), which are built on intuition or statistics of natural…”
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks (2017, methodology): “[8], many methods [44][42][28][3] has been developed over the last ten years.”
From Motion Blur to Motion Flow: A Deep Learning Solution for Removing Heterogeneous Motion Blur (2016, background): “Numerous image priors or regularizers have been studied to overcome the ill-posed nature of the problem, such as the total variational regularizer [5, 29], Gaussian scale mixture priors [9] and 1/ 2-norms [19], 0-norms [44, 27], and dark channel [28] based regularizers.”
CLEARER: Multi-Scale Neural Architecture Search for Image Restoration (2020, methodology): “To recover the clean image from the observed noisy image, a huge number of image restoration methods [30, 1, 5, 10] have been proposed based on different handcrafted priors.”
A Comparative Study for Single Image Blind Deblurring (2016, background): “…datasets do not consider several crucial factors, e.g., scene depth variation , sensor saturation and nonlinear camera response Blurred input image Cho & Lee [2] Krishnan et al. [15] Whyte et al. [43] Sun et al. [38] Xu et al. [45] Zhong et al. [49] Pan et al. [29] Perrone et al. [31] Figure 1.”