A weighted space restoration algorithm using the aperiodic model of deconvolution

2002 ◽  
Author(s):  
Zou Mou-Yan ◽  
R. Unbehauen
Keyword(s):  
Weighted Space ◽  
Restoration Algorithm

Image Restoration Algorithm for Single Nighttime Weakly Illuminated Haze Image

2018 ◽  
Vol 30 (3) ◽  
pp. 459
Author(s):  
Chunming Tang ◽  
Yancheng Dong ◽  
Xin Sun ◽  
Jun Lin ◽  
Zheng Lian
Keyword(s):  
Image Restoration ◽  
Restoration Algorithm

Approximation properties of Szász type operators involving Charlier polynomials

Filomat ◽  
2017 ◽  
Vol 31 (2) ◽  
pp. 479-487
Author(s):  
Didem Arı
Keyword(s):  
Asymptotic Formula ◽  
Weighted Space ◽  
Approximation Properties ◽  
Charlier Polynomials

In this paper, we give some approximation properties of Sz?sz type operators involving Charlier polynomials in the polynomial weighted space and we give the quantitative Voronovskaya-type asymptotic formula.

scholarly journals On the focusing energy-critical inhomogeneous NLS: Weighted space approach

2021 ◽  
Vol 205 ◽  
pp. 112261
Author(s):  
Yonggeun Cho ◽  
Kiyeon Lee
Keyword(s):  
Weighted Space ◽  
Space Approach

scholarly journals Compressed sensing image restoration algorithm based on improved SURF operator

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 1033-1045
Author(s):  
Guodong Zhou ◽  
Huailiang Zhang ◽  
Raquel Martínez Lucas
Keyword(s):  
Compressed Sensing ◽  
Image Restoration ◽  
Feature Vector ◽  
Optimization Method ◽  
Image Feature ◽  
Diffusion Method ◽  
Local Similarity ◽  
Weighting Method ◽  
Data Smoothing ◽  
Restoration Algorithm

Abstract Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.

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