Results 31 to 40 of about 293,019 (211)
Blur2Sharp: A GAN-Based Model for Document Image Deblurring
International Journal of Computational Intelligence Systems, 2021 The advances in mobile technology and portable cameras have facilitated enormously the acquisition of text images. However, the blur caused by camera shake or out-of-focus problems may affect the quality of acquired images and their use as input for ...
Hala Neji +4 more
doaj +1 more source
Blind Deblurring of Hyperspectral Document Images
, 2022 This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 101026453. This work is published in the Lecture Notes in Computer Science book series (LNCS, volume 13373) as part of the Image Analysis and Processing, ICIAP 2022 ...
Marina Ljubenovic +3 more
openaire +4 more sources
Semi-blind sparse image reconstruction with application to MRFM [PDF]
, 2012 We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known.
Hero, Alfred O. +2 more
core +1 more source
Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors
IEEE Access, 2023 The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models.
Zhe Li +3 more
doaj +1 more source
Burst Ranking for Blind Multi-Image Deblurring [PDF]
IEEE Transactions on Image Processing, 2020 We propose a new incremental aggregation algorithm for multi-image deblurring with automatic image selection. The primary motivation is that current bursts deblurring methods do not handle well situations in which misalignment or out-of-context frames are present in the burst.
Fidel Alejandro Guerrero Peña +4 more
openaire +4 more sources
Variational semi-blind sparse deconvolution with orthogonal kernel bases and its application to MRFM [PDF]
, 2014 We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known.
Se Un Parka +5 more
core +1 more source
A Blur Restoration Algorithm Based on L0 Regularization [PDF]
Jisuanji gongcheng, 2016 Aiming at the motion blur,a new blind deblurring algorithm is proposed,which is based on the L0 regularization restraints and the prior knowledge of natural image gradient distribution to obtain the real motion kernel.In the proposed methods,T-smooth ...
FANG Shuai,FAN Dong,YU Lei,CAO Fengyun
doaj +1 more source
CoRR, 2019 Deblurring is the task of restoring a blurred image to a sharp one, retrieving the information lost due to the blur. In blind deblurring we have no information regarding the blur kernel. As deblurring can be considered as an image to image translation task, deep learning based solutions, including the ones which use GAN (Generative Adversarial Network),
Manoj Kumar Lenka +2 more
openaire +2 more sources
Blind deblurring of foreground-background images [PDF]
2009 16th IEEE International Conference on Image Processing (ICIP), 2009 This paper presents a method for deblurring an image consisting of two layers (a foreground layer and a background layer) which have suffered different, unknown blurs. This is a situation of practical interest. For example, it is common to find images in which we have a foreground object (e.g. a car) which has motion blur while the background is sharp (
Mariana S. C. Almeida, Luís B. Almeida
openaire +1 more source
Blind Image Deblurring: a Review
CoRR, 2022 This is a review on blind image deblurring. First, we formulate the blind image deblurring problem and explain why it is challenging. Next, we bring some psychological and cognitive studies on the way our human vision system deblurs. Then, relying on several previous reviews, we discuss the topic of metrics and datasets, which is non-trivial to blind ...
openaire +3 more sources