Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling
The compressive sensing (CS) scheme exploits many fewer measurements than suggested
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
Event-based fusion for motion deblurring with cross-modal attention
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure
times. As a kind of bio-inspired camera, the event camera records the intensity changes in …
times. As a kind of bio-inspired camera, the event camera records the intensity changes in …
A Comprehensive Review of Blind Deconvolution Techniques for Image Deblurring.
P Satish, M Srikantaswamy… - Traitement du …, 2020 - search.ebscohost.com
Image Deblurring is a very popular area of research in all over the world. It is an illposed
problem which still does not have an ideal solution. Therefore, in order to analyse the …
problem which still does not have an ideal solution. Therefore, in order to analyse the …
Graph-based blind image deblurring from a single photograph
Blind image deblurring, ie, deblurring without knowledge of the blur kernel, is a highly ill-
posed problem. The problem can be solved in two parts: 1) estimate a blur kernel from the …
posed problem. The problem can be solved in two parts: 1) estimate a blur kernel from the …
Estimating generalized gaussian blur kernels for out-of-focus image deblurring
Out-of-focus blur is a common image degradation phenomenon that occurs in case of lens
defocusing. The out-of-focus blur kernel is usually modeled as a Gaussian function or a …
defocusing. The out-of-focus blur kernel is usually modeled as a Gaussian function or a …
Learning iteration-wise generalized shrinkage–thresholding operators for blind deconvolution
Salient edge selection and time-varying regularization are two crucial techniques to
guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However …
guarantee the success of maximum a posteriori (MAP)-based blind deconvolution. However …
Learned perceptual image enhancement
H Talebi, P Milanfar - 2018 IEEE international conference on …, 2018 - ieeexplore.ieee.org
Learning a typical image enhancement pipeline involves minimization of a loss function
between enhanced and reference images. While L 1 and L 2 losses are perhaps the most …
between enhanced and reference images. While L 1 and L 2 losses are perhaps the most …
Blind remote sensing image deblurring using local binary pattern prior
Z Zhang, L Zheng, Y Piao, S Tao, W Xu, T Gao, X Wu - Remote Sensing, 2022 - mdpi.com
In this paper, an algorithm based on local binary pattern (LBP) is proposed to obtain clear
remote sensing images under the premise of unknown causes of blurring. We find that LBP …
remote sensing images under the premise of unknown causes of blurring. We find that LBP …
FPM-WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction
Fourier ptychographic microscopy (FPM) theoretically provides a solution to the trade-off
between spatial resolution and field of view (FOV), and has promising prospects in digital …
between spatial resolution and field of view (FOV), and has promising prospects in digital …
Spatially-variant CNN-based point spread function estimation for blind deconvolution and depth estimation in optical microscopy
A Shajkofci, M Liebling - IEEE Transactions on Image …, 2020 - ieeexplore.ieee.org
Optical microscopy is an essential tool in biology and medicine. Imaging thin, yet non-flat
objects in a single shot (without relying on more sophisticated sectioning setups) remains …
objects in a single shot (without relying on more sophisticated sectioning setups) remains …