Results 1 to 10 of about 9,413,043 (217)

Noise-Adaptive Non-Blind Image Deblurring [PDF]

open access: yesSensors, 2022
This work addresses the problem of non-blind image deblurring for arbitrary input noise. The problem arises in the context of sensors with strong chromatic aberrations, as well as in standard cameras, in low-light and high-speed scenarios.
Michael Slutsky
doaj   +6 more sources

Spectral Norm Regularization for Blind Image Deblurring [PDF]

open access: yesSymmetry, 2021
Blind image deblurring is a well-known ill-posed inverse problem in the computer vision field. To make the problem well-posed, this paper puts forward a plain but effective regularization method, namely spectral norm regularization (SN), which can be regarded as the symmetrical form of the spectral norm.
Jianlin Zhang, Zhang Jianlin
exaly   +4 more sources

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring [PDF]

open access: yesSensors, 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   +4 more sources

Blind Image Deblurring Based on Local Edges Selection

open access: yesApplied Sciences, 2019
The edges of images are less sparse when images become blurred. Selecting effective image edges is a vital step in image deblurring, which can help us to build image deblurring models more accurately.
Yue Han, Jiangming Kan
doaj   +4 more sources

Gradient-wise search strategy for blind image deblurring [PDF]

open access: yesMATEC Web of Conferences, 2022
Blind image deblurring is a long-standing challenging problem to improve the sharpness of an image as a prerequisite step. Many iterative methods are widely used for the deblurring image, but care must be taken to ensure that the methods have fast ...
Wang Yunhong, Liu Dan
doaj   +2 more sources

Blind Deblurring Based on Sigmoid Function

open access: yesSensors, 2021
Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm
Shuhan Sun   +3 more
doaj   +2 more sources

Blind Image Deblurring via a Novel Sparse Channel Prior

open access: yesMathematics, 2022
Blind image deblurring (BID) is a long-standing challenging problem in low-level image processing. To achieve visually pleasing results, it is of utmost importance to select good image priors. In this work, we develop the ratio of the dark channel prior (
Dayi Yang, Xiaojun Wu, Hefeng Yin
doaj   +3 more sources

Overview of Blind Deblurring Methods for Single Image [PDF]

open access: yesJisuanji kexue yu tansuo, 2022
Image deblurring has been a research hotspot in computer vision and image processing for a long time. The motion blur or focus blur image caused by camera jitter, object motion or defocus will seriously affect the use and follow-up processing of the ...
LIU Liping, SUN Jian, GAO Shiyan
doaj   +1 more source

Blind Deblurring Based on a Single Luminance Channel and L1-Norm

open access: yesIEEE Access, 2021
To improve the image quality of the deblurring results restored by existing blind deblurring method, an effective image blind deblurring method based on a single channel and L1-norm is proposed for the blurry images.
Luoyu Zhou, Zhiyang Liu
doaj   +1 more source

Blind deblurring of natural images [PDF]

open access: yes2008 IEEE International Conference on Acoustics, Speech and Signal Processing, 2008
A new method to perform blind image deblurring is proposed. Very few assumptions are made on the blurring filter and on the original image: the blurring filter is assumed to have limited support and the original image is assumed to be a sharp natural image. A new prior is used, which gives higher probability to images with sharp edges.
Mariana S. C. Almeida, Luís B. Almeida
openaire   +1 more source

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