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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
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Reconstruction of noisy and blurred images using blur kernel
IOP Conference Series: Materials Science and Engineering, 2017 Blur is a common in so many digital images. Blur can be caused by motion of the camera and scene object. In this work we proposed a new method for deblurring images. This work uses sparse representation to identify the blur kernel. By analyzing the image coordinates Using coarse and fine, we fetch the kernel based image coordinates and according to ...
Vijayan Ellappan, Vishal Chopra
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Robust Image Restoration for Motion Blur of Image Sensors
Sensors, 2016 Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly.
Fasheng Yang +4 more
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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
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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
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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
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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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A New Full-Reference Image Quality Metric for Motion Blur Profile Characterization
IEEE Access, 2021 Motion blur is common in images captured by handheld devices, arising from hand, device and/or object motion. To restore sharp images from the images degraded by the motion, it is extremely important to assess the quality of the captured image and its ...
Mohammad Abdullah-Al-Mamun +2 more
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Combined Invariants to Gaussian Blur and Affine Transformation [PDF]
, 2021 The paper presents a new theory of combined moment invariants to Gaussian blur and spatial affine transformation. The blur kernel may be arbitrary oriented, scaled and elongated.
Jan Flusser +5 more
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Explore Image Deblurring via Blur Kernel Space
CoRR, 2021 Accepted to CVPR ...
Phong Tran +3 more
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