Results 41 to 50 of about 9,140,641 (221)
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
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Survey on Image Deblurring Algorithms [PDF]
Jisuanji kexueImage deblurring is a classic problem in computer vision,aiming to recover sharp visual information from blurry input images or videos.Blur is often caused by factors such as camera misfocus,camera shake,or fast-moving objects.Traditional deblurring ...
CHEN Kang, LIN Jianhan, LIU Yuanjie
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Microscopy Today, 1998 Abstract If you are an optical microscopist, chances are you have sometimes wished for a way to increase the depth of focus of your images. In this article I describe a method that does this using a simple combination of functions built into most image processing software - so it will not cost you very much to try, The method, however ...
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COMBINED PATCH-WISE MINIMAL-MAXIMAL PIXELS REGULARIZATION FOR DEBLURRING [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Deblurring is a vital image pre-processing procedure to improve the quality of images. It is a classical ill-posed problem. A new blind deblurring method based on image sparsity prior is proposed here.
J. Han, S. L. Zhang, Z. Ye
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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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Blind image deblurring method based on
Journal of Measurement Science and Instrumentation, 2023 Aiming at the problem of ringing artifacts existing in the edge of image in traditional blind image deblurring methods, l1/l2 regularization-based blind image deblurring method is proposed. The latent image is constrained by l1/l2 regularization, and the
CAO Shengfang, HU Hongping, WANG Wenke
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Joint Image Deblurring and Matching with Blurred Invariant-Based Sparse Representation Prior
Complexity, 2019 Image matching is important for vision-based navigation. However, most image matching approaches do not consider the degradation of the real world, such as image blur; thus, the performance of image matching often decreases greatly. Recent methods try to
Yuanjie Shao +3 more
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SID: Sensor-Assisted Image Deblurring System for Mobile Devices
IEEE Access, 2019 Handheld mobile photography is often affected by motion blur due to the difficulty of keeping the camera's stable. The existing processing method is usually a high-cost deblurring process of a computer, which seriously affects the user experience, and ...
Qing Wang +4 more
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Thermal Image Reconstruction Using Deep Learning
IEEE Access, 2020 A high-resolution thermal camera is very expensive and is thus difficult to be used. Furthermore, thermal images become blurred in various cases of object motion, camera shaking, and camera defocusing. To solve these problems, a previous super-resolution
Ganbayar Batchuluun +4 more
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Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 As a fundamental preprocessing technique, remote sensing image motion deblurring is important for visual understanding tasks. Most conventional approaches formulate the image motion deblurring task as a kernel estimation. Because the kernel estimation is
Jie Fang +3 more
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