Jul 5, 2015 · In this paper, we study the image deblurring problem based on sparse representation over learned dictionary which leads to promising ...
In this paper, we study the image deblurring problem based on sparse representation over learned dictionary which leads to promising performance in image ...
[28] performs the non-blind deblurring step using the Richardson-Lucy algorithm. [17] models a spatially variant blur operator as being sparse in a wavelet.
PDF | We propose and test a simple algorithmic framework for recovering images from blurry and noisy observations based on total variation (TV).
Then, we propose a structured sparse representation model based on this new transformation matrix for image restoration, taking advantage of the sparse ...
Missing: Via | Show results with:Via
Jun 26, 2023 · To address this, our study proposes a mathematical model for non-blind image deblurring based on total fractional-order variational principles.
Image Deblurring via Total Variation Based Structured Sparse Model Selection ; Tieyong zeng; Ma LY(马丽艳) ; 刊名, Journal of Scientific Computing ; 2016-04-01.
We propose a variational model for artifact-free JPEG decompression. It is based on the minimization of the total variation over the convex set U of all ...
Abstract— In recent years sparse representation model (SRM) based image deblurring approaches have shown promising image deblurring results.