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Noise-Robust Iterative Back-Projection

Authors
Yoo, Jun-SangKim, Jong-Ok
Issue Date
2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Image reconstruction; Noise measurement; Principal component analysis; Image resolution; Noise robustness; Optimization; Noisy image; back-projection; super-resolution; texture; PCA; sparsity; cost optimization
Citation
IEEE TRANSACTIONS ON IMAGE PROCESSING, v.29, pp.1219 - 1232
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume
29
Start Page
1219
End Page
1232
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/58983
DOI
10.1109/TIP.2019.2940414
ISSN
1057-7149
Abstract
Noisy image super-resolution (SR) is a significant challenging process due to the smoothness caused by denoising. Iterative back-projection (IBP) can be helpful in further enhancing the reconstructed SR image, but there is no clean reference image available. This paper proposes a novel back-projection algorithm for noisy image SR. Its main goal is to pursuit the consistency between LR and SR images. We aim to estimate the clean reconstruction error to be back-projected, using the noisy and denoised reconstruction errors. We formulate a new cost function on the principal component analysis (PCA) transform domain to estimate the clean reconstruction error. In the data term of the cost function, noisy and denoised reconstruction errors are combined in a region-adaptive manner using texture probability. In addition, the sparsity constraint is incorporated into the regularization term, based on the Laplacian characteristics of the reconstruction error. Finally, we propose an eigenvector estimation method to minimize the effect of noise. The experimental results demonstrate that the proposed method can perform back-projection in a more noise-robust manner than the conventional IBP, and harmoniously work with any other SR methods as a post-processing.
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공과대학 (전기전자공학부)
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