Varational method using the Kuan filtering approach for the restoration of blurred images with multiplicative noise

AbstractA new hybrid variational model for recovering blurred images in the presence of multiplicative noise is proposed. Inspired by previous work on multiplicative noise removal, an I-divergence technique is used to build a strictly convex model under a condition that ensures the uniqueness of the solution and the stability of the algorithm. A split-Bregman algorithm is adopted to solve the constrained minimisation problem in the new hybrid model efficiently. Numerical tests for simultaneous deblurring and denoising of the images subject to multiplicative noise are then reported. Comparison with other methods clearly demonstrates the good performance of our new approach.

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A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise

Mathematical Problems in Engineering ◽

10.1155/2020/1952782 ◽

2020 ◽

Vol 2020 ◽

pp. 1-19

Author(s):

Hanmei Yang ◽

Jiachang Li ◽

Lixin Shen ◽

Jian Lu

Keyword(s):

Multiplicative Noise ◽

Variational Model ◽

Total Variation Regularization ◽

Dual Algorithm ◽

Penalty Term ◽

Mathematical Properties ◽

Proposed Model ◽

Primal Dual ◽

Data Fidelity ◽

Blurred Images

This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise. Considering the statistical property of the multiplicative noise following Nakagami distribution, the denoising model consists of a data fidelity term, a quadratic penalty term, and a total variation regularization term. Here, the quadratic penalty term is mainly designed to guarantee the model to be strictly convex under a mild condition. Furthermore, the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator. We also study some mathematical properties of the proposed model. In addition, the model is solved by applying the primal-dual algorithm. The experimental results show that the proposed method is promising in restoring (blurred) images with multiplicative noise.

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A Convex Variational Model for Restoring Blurred Images with Multiplicative Noise

SIAM Journal on Imaging Sciences ◽

10.1137/120870621 ◽

2013 ◽

Vol 6 (3) ◽

pp. 1598-1625 ◽

Cited By ~ 63

Author(s):

Yiqiu Dong ◽

Tieyong Zeng

Keyword(s):

Multiplicative Noise ◽

Variational Model ◽

Blurred Images

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Deblurring noisy radial-blurred images: spatially adaptive filtering approach

10.1117/12.769400 ◽

2008 ◽

Cited By ~ 3

Author(s):

Giacomo Boracchi ◽

Alessandro Foi ◽

Vladimir Katkovnik ◽

Karen Egiazarian

Keyword(s):

Adaptive Filtering ◽

Spatially Adaptive ◽

Blurred Images ◽

Filtering Approach

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Recognition of Birds in Blurred and Illumination Images by Local Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0128 ◽

2017 ◽

Vol 7 (7) ◽

pp. 243

Author(s):

Suresha .M ◽

. Sandeep

Keyword(s):

Computer Vision ◽

Time Complexity ◽

Feature Detection ◽

Feature Matching ◽

Fast Algorithms ◽

Local Features ◽

Experimental Demonstration ◽

Harris Corner ◽

Blurred Image ◽

Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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