Results 31 to 40 of about 15,547 (189)
Image Deblurring Based on Convex Non-Convex Sparse Regularization and Plug-and-Play Algorithm
Algorithms, 2023 Image deblurring based on sparse regularization has garnered significant attention, but there are still certain limitations that need to be addressed.
Yi Wang +4 more
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Learning Wavefront Coding for Extended Depth of Field Imaging [PDF]
, 2020 Depth of field is an important factor of imaging systems that highly affects the quality of the acquired spatial information. Extended depth of field (EDoF) imaging is a challenging ill-posed problem and has been extensively addressed in the literature ...
Akpinar, Ugur +4 more
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A domain translation network with contrastive constraint for unpaired motion image deblurring
IET Image Processing, 2023 Most motion deblurring methods require a large amount of paired training data, which is nearly unreachable in practice. To overcome the limitation, a domain translation network with contrastive constraint for unpaired motion image deblurring is proposed.
Bingxin Zhao, Weihong Li
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IEEE Access, 2020 We propose an $L_{p}$ -norm-based sparse regularization model for license plate deblurring, which is motivated by distinctive properties of license plate images. For the blurred images, general deblurring methods may restore a good overall visual effect.
Chenping Zhao +4 more
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IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016 We describe a learning-based approach to blind image deconvolution. It uses a deep layered architecture, parts of which are borrowed from recent work on neural network learning, and parts of which incorporate computations that are specific to image deconvolution.
Schuler, C. +3 more
openaire +5 more sources
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
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 Blind deblurring consists a long studied task, however the outcomes of generic methods are not effective in real world blurred images. Domain-specific methods for deblurring targeted object categories, e.g. text or faces, frequently outperform their generic counterparts, hence they are attracting an increasing amount of attention.
Chrysos, GG, Zafeiriou, S
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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
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Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity
Applied Sciences, 2019 The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by neural network
Minsoo Hong, Yoonsik Choe
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Iterative Blind Deconvolution Algorithm for Deblurring a Single PSP/TSP Image of Rotating Surfaces
Sensors, 2018 Imaging of pressure-sensitive paint (PSP) for pressure measurement on moving surfaces is problematic due to the movement of the object within the finite exposure time of the imager, resulting in the blurring of the blade edges.
Anshuman Pandey, James W. Gregory
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