Browse > Article
http://dx.doi.org/10.17662/ksdim.2020.16.3.049

Non-Local Mean based Post Processing Scheme for Performance Enhancement of Image Interpolation Method  

Kim, Donghyung (한양여자대학교 컴퓨터정보과)
Publication Information
Journal of Korea Society of Digital Industry and Information Management / v.16, no.3, 2020 , pp. 49-58 More about this Journal
Abstract
Image interpolation, a technology that converts low resolution images into high resolution images, has been widely used in various image processing fields such as CCTV, web-cam, and medical imaging. This technique is based on the fact that the statistical distributions of the white Gaussian noise and the difference between the interpolated image and the original image is similar to each other. The proposed algorithm is composed of three steps. In first, the interpolated image is derived by random image interpolation. In second, we derive weighting functions that are used to apply non-local mean filtering. In the final step, the prediction error is corrected by performing non-local mean filtering by applying the selected weighting function. It can be considered as a post-processing algorithm to further reduce the prediction error after applying an arbitrary image interpolation algorithm. Simulation results show that the proposed method yields reasonable performance.
Keywords
Image Interpolation; Non-Local Mean Filtering; Weight Function; Post-Processing; Nearest Neighbor Interpolation; Bi-Linear Interpolation; Bi-Cubic Interpolation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 F. A. Jassim and F. H Altaany., "Image Interpolation Using Kriging Technique For Spatial Data," Canadian Journal on Image Processing and Computer Vision, Vol.4, No.2, 2013, pp.16-21.
2 R Roy., M Pal. and T Gulati., "Zooming Digital Images Using Interpolation Techniques," International Journal of Application or Innovation in Engineering & Management (IJAIEM), Vol.2, No.4, 2013, pp.34-45.
3 M. R Choi, S. J. Ko, and G. R. Kwon, "Color Image Interpolation in the DCT Domain Using a Wavelet-based Differential Value," Multimedia Tools and Applications, Vol.77, No.16, 2018, pp.21539-21556.   DOI
4 S. R. Aswathy, C. Reshmalakshmi, "Enhanced DCT Interpolation for Better 2D Image Up-Sampling," International Journal of Engineering Research & Technology, Vol.4, No.6, 2015, pp.672-676.
5 H. Demirel, G. Anbarjafari, "Image Resolution Enhancement By Using Discrete and Stationary Wavelet Decomposition.," IEEE Transaction on Image Process, Vol.20, No.5, 2011, pp.1458-1460.   DOI
6 R. S Asamwar. K. M. Bhurchandi and A. S Gandhi., "Interpolation of Images Using Discrete Wavelet Transform to Simulate Image Resizing as in Human Vision," International Journal of Automation and Computing, Vol.7, No.1, 2010, pp.9-16.   DOI
7 Y. C. Fan and Y. F. Chiang, "Discrete Wavelet Transform on Color Picture Interpolation of Digital Still Camera," VLSI Design, Article ID 738057, 2013, pp.1-9.
8 김동형, "이미지 보간을 위한 의사결정나무 분류기법의 적용 및 구현," 디지털산업정보학회논문지, 제16권, 제1호, 2020, pp.55-65.   DOI
9 A. Buades, B. Coll, and J.M. Morel, "A Review of Image Denoising Algorithms, with a New One," Multiscale Modeling & Simulation, Vol.4, No.2, 2005, pp.490-530.   DOI
10 A. Buades, B. Coll, and J. M. Morel, "Image Denoising Methods. A New Nonlocal Principle," Siam Review, Vol.52, No.1, 2010, pp.113-147.   DOI
11 G. Gilboa and S. Osher, "Nonlocal Linear Image Regularization and Supervised Segmentation," Multiscale Modeling, Vol.6, No.2, 2007, pp.595-630.   DOI
12 S. Grewenig, S. Zimmer, and J. Weickert, "Rotationally Invariant Similarity Measures for Nonlocal Image Denoising," Journal of Visual Communication and Image Representation, Vol.22, No.2, 2011, pp.117-130.   DOI
13 J. Salmon, "On Two Parameters for Denoising with Non-local Means," Signal Processing Letters, Vol.17, No.3, 2010, pp.269-272.   DOI
14 T. Tasdizen, "Principal Neighborhoods Dictionaries for Nonlocal Means Image Denoising," IEEE Transactions on Image Processing, Vol.18, No.12, 2009, pp.2649-2660.   DOI