Deblurring Text Images via L0-Regularized Intensity and Gradient Prior

By Jin-shan Pan, Zhe Hu, Zhixun Su, Ming-Hsuan Yang. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014

483 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/60935eb7c4e23782ce231c798af11c917639c951

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Comparative Study for Single Image Blind Deblurring (2016, methodology): “However, existing 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. [”
Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels (2019, background): “6 shows the visual comparison with state-of-the-art SISR methods ( i.e ., RCAN [67], SRMD [63] and ZSSR [51]) on classical image “ chip ” [22], noisy image “ frog ” [33] and blurry image “ colour ” [43].”
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.”
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (2017, background): “From Figure 9, we can see that VDSR and SelfEx both tend to produce over-smoothed results, whereas SRMD can recover sharp image with better intensity and gradient statistics of clean images [35].”
Deep Image Deblurring: A Survey (2022, methodology): “(From left to right, top to bottom) input image, results from [142] [145], [18], [163], [16], [17], [94] and [40], respectively.”
Learning to Super-Resolve Blurry Face and Text Images (2017, methodology): “Methods [45]+[43] [45]+[8] [45]+[18] [32]+[43] [32]+[8] [32]+[18] [33]+[43] [33]+[8] [33]+[18]”
Dynamic Scene Deblurring With Parameter Selective Sharing and Nested Skip Connections (2019, background): “Many natural image priors [1, 27, 2, 35, 36, 21, 22] were proposed to regularize the solution space and advance the deblurring research.”
Scale-Recurrent Network for Deep Image Deblurring (2018, background): “uniform/non-uniform/depth-aware), and utilized different natural image priors [1, 3, 6, 39, 14, 40, 26, 27] to regularize the solution space.”
Blind Image Deblurring Using Dark Channel Prior (2016, methodology): “Levin et al. [19] show that deblurring methods based on this prior tend to favor blurry images over original clear images, especially for algorithms formulated within the maximum a posterior (MAP) framework.”
$L_0$ -Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond (2017, methodology): “(c) Deblurred result using [43] with fixed initial threshold 0:5 for s b .”
Unsupervised Domain-Specific Deblurring via Disentangled Representations (2019, methodology): “Conventional methods [28, 29, 27, 41, 14] still tend to under-deblur or over-deblur images, especially on local regions such as eyes and mouths.”
Stripformer: Strip Transformer for Fast Image Deblurring (2022, unknown): “Some works utilize prior assumptions to remove nonuniform blur [3,4,5].”