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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:

2023年03期

Page:

53-59

Research Field:

计算机科学与技术

Publishing date:

Info

Title:

Non-local Extension of Bicubic Interpolation in Image Super-resolution

Author(s):

Zheng Xincao¹; 2; Sun Zhonggui²

(1.Ji Xianlin Honors School, Liaocheng University, Liaocheng 252000, China)
(2.School of Mathematical Sciences, Liaocheng University, Liaocheng 252000, China)

Keywords:

image super-resolution;  bicubic;  non-local means;  periodicity

PACS:

TP391.41

DOI:

10.3969/j.issn.1672-1292.2023.03.007

Abstract:

Benefiting from its powerful ability in detail preseriving, bicubic interpolation has become a common algorithm in image super-resolution. The base function for bicubic interpolation is constructed by the 16 pixels that are closest to the current pixel in space, so the bicubic interpolation belongs to a local algorithm. This means that the interpolation process in this algorithm is not able to take advantage of the periodicity(non-locality)in natural images, and thus its performance still has room to be further improved. Based on this considerations, this paper introduces non-local weight correction into the definition of the base function to select more candidate pixels in a wider area to estimate the gray value of the current pixel. As a result, a non-local version of the typical bicubic interpolation is achieved. The experimental results suggest that it can reconstruct higher quality results both quantitatively and perceptually.

References:

[1]ZHANG K B,GAO X B,TAO D C,et al. Single image super-resolution with non-local means and steering kernel regression[J]. IEEE Transactions on Image Processing,2012,21(11):4544-4556.
[2]杜婉君,孙忠贵. 跨模态空域自适应联合均值滤波器[J]. 南京师范大学学报(工程技术版),2022,22(1):52-58.
[3]ZHAO F,HU Z F. Image super resolution reconstruction algorithm based on convolution neural network[J]. Journal of Physics:Conference Series,2021,2025:012033.
[4]LI M,NGUYEN T Q. Markov random field model-based edge-directed image interpolation[J]. IEEE Transactions on Image Processing,2008,17(7):1121-1128.
[5]CHENG Y,WANG Y,LIU Q. Research on digital image scaling based on bicubic filter algorithm[C]//Proceedings of the 2018 IEEE 3rd International Conference on Image,Vision and Computing(ICIVC). Chongqing,China:IEEE,2018.
[6]GAO X B,WANG Q,LI X L,et al. Zernike-moment-based image super resolution[J]. IEEE Transactions on Image Processing,2011,20(10):2738-2747.
[7]NAZREN A R A,YAAKOB S N,NGADIRAN R,et al. Improving iterative back projection super resolution model via anisotropic diffusion edge enhancement[C]//Proceedings of the 2016 International Conference on Robotics,Automation and Sciences(ICORAS). Melaka,Malaysia:IEEE,2016.
[8]ZHANG Y L,WEI D L,QIN C,et al. Context reasoning attention network for image super-resolution[C]//Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision(ICCV). Montreal,Canada:IEEE,2021.
[9]TONG T,LI G,LIU X J,et al. Image super-resolution using dense skip connections[C]//Proceedings of the 2017 IEEE International Conference on Computer Vision(ICCV). Venice,Italy:IEEE,2017.
[10]张宇苏,吴小俊,李辉,等. 基于无监督深度学习的红外图像与可见光图像融合算法[J]. 南京师范大学学报(工程技术版),2023,23(1):1-9.
[11]JIANG J J,CHEN C,MA J Y,et al. SRLSP:a face image super-resolution algorithm using smooth regression with local structure prior[J]. IEEE Transactions on Multimedia,2017,19(1):27-40.
[12]BUADES A,COLL B,MOREL J M. A non-local algorithm for image denoising[C]//Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR'05). San Diego,USA:IEEE,2005.
[13]KUMAR B K S. Image denoising based on non-local means filter and its method noise thresholding[J]. Signal Image and Video Processing,2013,7(6):1211-1227.
[14]SUN Z G,LYU M Q,LI J,et al. A local-nonlocal mathematical morphology[J]. Neurocomputing,2022,495:51-61.
[15]李兵,刘全升,徐家伟,等. 去除混合噪音的一种新方法[J]. 中国科学:信息科学,2010,40(9):1165-1175.
[16]WU W W,LIU F L,ZHANG Y B,et al. Non-local low-rank cube-based tensor factorization for spectral CT reconstruction[J]. IEEE Transactions on Medical Imaging,2019,38(4):1079-1093.
[17]KEYS R. Cubic convolution interpolation for digital image processing[J]. IEEE Transactions on Acoustics,Speech and Signal Processing,1981,29(6):1153-1160.
[18]裴银祥. 快速非局部均值图像去噪算法[D]. 郑州:郑州大学,2014.
[19]WANG X T,SHEN S S,SHI G M,et al. Iterative non-local means filter for salt and pepper noise removal[J]. Journal of Visual Communication and Image Representation,2016,38:440-450.
[20]周涛,刘珊,董雅丽,等. 肺部肿瘤跨模态图像融合的并行分解自适应融合模型[J]. 中国图象图形学报,2023,28(1):221-233.
[21]吴岸聪,林城梽,郑伟诗. 面向跨模态行人重识别的单模态自监督信息挖掘[J]. 中国图象图形学报,2022,27(10):2843-2859.

Memo

Memo:

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Common functions

Last Update: 2023-09-15