Results 31 to 40 of about 557,712 (297)
Understanding and evaluating blind deconvolution algorithms [PDF]
, 2009 Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand.The goal of this paper
Durand, Fredo +3 more
core +1 more source
CycleGAN With a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry [PDF]
IEEE Transactions on Computational Imaging, 2019 Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depends on the accuracy of a model and optimization algorithms.
Sungjun Lim +5 more
semanticscholar +1 more source
Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring
Sensors, 2017 Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process.
Naixue Xiong +5 more
doaj +1 more source
IEEE Access, 2020 Blind image deblurring, composed of estimating blur kernel and non-blind deconvolution, is an extremely ill-posed problem. However, previous deblurring methods still cannot solve delta kernel or noise problem well and avoid ringing artifacts in restored ...
Hongtian Zhao +3 more
doaj +1 more source
Cascaded Degradation-Aware Blind Super-Resolution
Sensors, 2023 Image super-resolution (SR) usually synthesizes degraded low-resolution images with a predefined degradation model for training. Existing SR methods inevitably perform poorly when the true degradation does not follow the predefined degradation ...
Ding Zhang +3 more
doaj +1 more source
IMAGE SHARPENING WITH BLUR MAP ESTIMATION USING CONVOLUTIONAL NEURAL NETWORK [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019 We propose a method for choosing optimal values of the parameters of image sharpening algorithm for out-of-focus blur based on grid warping approach. The idea of the considered sharpening algorithm is to move pixels from the edge neighborhood towards the
A. Nasonov, A. Krylov, D. Lyukov
doaj +1 more source
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal [PDF]
, 2015 In this paper, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional ...
Cao, Wenfei +3 more
core +5 more sources
An Improved Feedback Network Superresolution on Camera Lens Images for Blind Superresolution
Journal of Electrical and Computer Engineering, 2021 Most of the recent advances in image superresolution (SR) assume that the blur kernel during downsampling is predefined (e.g., Bicubic or Gaussian kernel), but it is a difficult task to make it suitable for all the realistic images.
Yuhao Liu
doaj +1 more source
Solar Speckle Image Deblurring With Deep Prior Constraint Based on Regularization
IEEE Access, 2022 The solar speckle image has the characteristics with single features, more noise, and blurred local details. Most of the existing deep learning deblurring methods for solar speckle images have some problems, such as high-frequency loss, artifact ...
Yahui Jin +5 more
doaj +1 more source
Blur kernel estimation of noisy-blurred image via dynamic structure prior
Neurocomputing, 2020 Abstract An accurate blur kernel is key to blind image deblurring and kernel estimation heavily relies on strong edges in the observed image [ 1 , 2, 3]. Previous methods [4] [5] leveraging image gradient prior with i.i.d statistics can hardly restrict strong edges in a noisy-blurred image, since both noise and strong edges are presented as strong ...
Xueling Chen +4 more
openaire +2 more sources