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$L_0$ -Regularized Intensity and Gradient Prior for Deblurring Text Images and Beyond
We propose a simple yet effective <inline-formula><tex-math notation="LaTeX">$L_0$</tex-math><alternatives> <inline-graphicExpand
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Mathematical Problems in Data Science
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Geometry-based 2D shape descriptor for retrieval in large database
Most of the existing shape retrieval methods need a one-to-one shape descriptor matching procedure to achieve a high retrieval rate. However, high performance shape matching methods are usuallyExpand
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Embedding Non-Local Mean in Squeeze-and-Excitation Network for Single Image Deraining
Images captured in rainy outdoor usually have poor visual quality due to the appearance of raindrops blur or rain streaks in the image. For many practical vision systems, such as autonomous drivingExpand
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An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming
In recent years, numerous vision and learning tasks have been (re)formulated as nonconvex and nonsmooth programmings(NNPs). Although some algorithms have been proposed for particular problems,Expand
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L 0-Regularized Intensity and Gradient Prior for Text Images Deblurring and Beyond Supplemental Material
1 QUANTITATIVE EVALUATION ON TEXT IMAGE DATASET In this section, we have created a dataset containing 15 images and 8 blur kernels extracted from Levin et al. [1]. Similar to [1], we can generate 120Expand
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Blind Image Deblurring with Outlier Handling Supplemental Material
In this supplemental material, we analyze how the proposed blind image deblurring method performs on images with outliers and demonstrate the effectiveness of the proposed data fidelity term inExpand
NEAS: Nonuniform Extraction of Attractive Structures for Image Analysis
The attractive structures in human perception often correspond to objects of interest and have great practical importance. Therefore, extracting the attractive structures from images is a fundamentalExpand
Local Connectivity Enhanced Sparse Representation
During the past two decades, the subspace clustering problem has attracted much attention. Since the data set in real-world problems usually contains a lot of categories, it seems that the largeExpand
Bayesian rank penalization
Rank minimization is a key component of many computer vision and machine learning methods, including robust principal component analysis (RPCA) and low-rank representations (LRR). However, usualExpand
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