Noise-robust iterative back-projection

Jun Sang Yoo; Jong Ok Kim

doi:10.1109/TIP.2019.2940414

Noise-robust iterative back-projection

Jun Sang Yoo, Jong Ok Kim

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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.

Original language	English
Article number	8839728
Pages (from-to)	1219-1232
Number of pages	14
Journal	IEEE Transactions on Image Processing
Volume	29
DOIs	https://doi.org/10.1109/TIP.2019.2940414
Publication status	Published - 2020

Bibliographical note

Funding Information:
Manuscript received September 28, 2018; revised March 8, 2019, May 23, 2019, and September 4, 2019; accepted September 4, 2019. Date of publication September 16, 2019; date of current version November 4, 2019. This work was supported in part by the National Research Foundation of Korea (NRF) through the Korean Government Ministry of Science and Information and Communication Technology (MSIT) under Grant NRF-2019R1A2C1005834, and in part by the MSIT, South Korea, under the Information Technology Research Center (ITRC) Support Program supervised by the Institute of Information & Communications Technology Planning & Evaluation (IITP) under Grant IITP-2019-2018-0-01421. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Dr. Aline Roumy. (Corresponding author: Jong-Ok Kim.) The authors are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/TIP.2019.2940414

Publisher Copyright:
© 1992-2012 IEEE.

Keywords

Noisy image
PCA
back-projection
cost optimization
sparsity
super-resolution
texture

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

Access to Document

10.1109/TIP.2019.2940414

Cite this

@article{b5524d5e3b844846bde99930f2e8d165,

title = "Noise-robust iterative back-projection",

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.",

keywords = "Noisy image, PCA, back-projection, cost optimization, sparsity, super-resolution, texture",

author = "Yoo, {Jun Sang} and Kim, {Jong Ok}",

note = "Funding Information: Manuscript received September 28, 2018; revised March 8, 2019, May 23, 2019, and September 4, 2019; accepted September 4, 2019. Date of publication September 16, 2019; date of current version November 4, 2019. This work was supported in part by the National Research Foundation of Korea (NRF) through the Korean Government Ministry of Science and Information and Communication Technology (MSIT) under Grant NRF-2019R1A2C1005834, and in part by the MSIT, South Korea, under the Information Technology Research Center (ITRC) Support Program supervised by the Institute of Information & Communications Technology Planning & Evaluation (IITP) under Grant IITP-2019-2018-0-01421. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Dr. Aline Roumy. (Corresponding author: Jong-Ok Kim.) The authors are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/TIP.2019.2940414 Publisher Copyright: {\textcopyright} 1992-2012 IEEE.",

year = "2020",

doi = "10.1109/TIP.2019.2940414",

language = "English",

volume = "29",

pages = "1219--1232",

journal = "IEEE Transactions on Image Processing",

issn = "1057-7149",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - JOUR

T1 - Noise-robust iterative back-projection

AU - Yoo, Jun Sang

AU - Kim, Jong Ok

N1 - Funding Information: Manuscript received September 28, 2018; revised March 8, 2019, May 23, 2019, and September 4, 2019; accepted September 4, 2019. Date of publication September 16, 2019; date of current version November 4, 2019. This work was supported in part by the National Research Foundation of Korea (NRF) through the Korean Government Ministry of Science and Information and Communication Technology (MSIT) under Grant NRF-2019R1A2C1005834, and in part by the MSIT, South Korea, under the Information Technology Research Center (ITRC) Support Program supervised by the Institute of Information & Communications Technology Planning & Evaluation (IITP) under Grant IITP-2019-2018-0-01421. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Dr. Aline Roumy. (Corresponding author: Jong-Ok Kim.) The authors are with the School of Electrical Engineering, Korea University, Seoul 02841, South Korea (e-mail: [email protected]; [email protected]). Digital Object Identifier 10.1109/TIP.2019.2940414 Publisher Copyright: © 1992-2012 IEEE.

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Noisy image

KW - PCA

KW - back-projection

KW - cost optimization

KW - sparsity

KW - super-resolution

KW - texture

UR - http://www.scopus.com/inward/record.url?scp=85077500791&partnerID=8YFLogxK

U2 - 10.1109/TIP.2019.2940414

DO - 10.1109/TIP.2019.2940414

M3 - Article

AN - SCOPUS:85077500791

SN - 1057-7149

VL - 29

SP - 1219

EP - 1232

JO - IEEE Transactions on Image Processing

JF - IEEE Transactions on Image Processing

M1 - 8839728

ER -

Noise-robust iterative back-projection

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this