Super Resolution-based Image Enhancement System

doi:10.3969/j.issn.1006-2475.2014.05.021

Abstract

Abstract: To enhance image resolution, and also to provide a comparison and analysis tool for image enhancement algorithms, a Super Resolution-based Image Enhancement System (SR-IES) is constructed and implemented, which combines image degradation simulation, gray level enhancement, super resolution (SR) image reconstruction and image evaluation into one. SR-IES enhances images using multiple image processing methods, among which SR reconstruction algorithms are key technologies of this system. System tests show that SR-IES can improve resolution and visual effect of images effectively and provide a basis for algorithms comparison through image quality assessments.


Key words: image enhancement, super resolution, image evaluation, software system

CLC Number:

TP317.4

LI Wei-xiang, LI Zhan, WU Shao-chun, DENG Fu-ming. Super Resolution-based Image Enhancement System[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2014.05.021.

References ［19］

［1］	Tsai R Y, Huang T S. Multiframe image restoration and registration［J］. Advances in Computer Vision and Image Processing, 1984,1:317-339
［2］	Vandewalle P. Super-resolution from Unregistered Dliased Images［D］. Switzerland: Ecole Polytechnique Fédérale de Lausanne, 2006.
［3］	The Ecole Polytechnique Federal de Lausanne. Super-Resolution［DB/OL］. http://lcavwww.epfl.ch/software/superresolution, 2012-04-06.
［4］	Amintoosi M, Fathy M, Mozayani N. Regional varying image super-resolution［C］// International Joint Conference on Computational Sciences and Optimization, 2009. 2009,1:913-917.
［5］	Su H, Wu Y, Zhou J. Super-resolution without dense flow［J］. IEEE Transactions on Image Processing, 2012,21(4):1782-1795
［6］	Dong W S, Zhang D, Shi G M, et al. Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization［J］. IEEE Transactions on Image Processing, 2011,20(7):1838-1857.
［7］	Hung K W, Siu W C. Robust soft-decision interpolation using weighted least squares［J］. IEEE Transactions on Image Processing, 2012,21(3):1061-1069.
［8］	杨宇翔. 图像超分辨率重建算法研究［D］. 合肥:中国科学技术大学, 2013.
［9］	肖思达,刘伟俭. 图像超分辨率处理方法和系统：中国, CN103500436A［P］, 2014-01-08.
［10］	Li Z, Han G, Xiao S, et al. MAP-based single-frame super-resolution image reconstruction for license plate recognition［C］// International Conference on Computational Intelligence and Software Engineering, 2009. 2009:1-5.
［11］	李展,张庆丰,孟小华,等. 多分辨率图像序列的超分辨率重建［J］. 自动化学报, 2012,38(11):1804-1814.
［12］	Park S C, Park M K, Kang M G. Super-resolution image reconstruction: A technical overview［J］. IEEE Signal Processing Magazine, 2003,20(3):21-36.
［13］	黄果,许黎,蒲亦非. 分数阶微积分在图像处理中的研究综述［J］. 计算机应用研究, 2012,29(2):414-420,426.
［14］	Schultz R R, Stevenson R L. A Bayesian approach to image expansion for improved definition［J］. IEEE Transactions on Image Processing, 1994,3(3):233-242.
［15］	梁鹏,黎绍发,覃姜维. 基于局部类别一致K均值聚类的目标识别方法［J］. 华南理工大学学报:自然科学版, 2011,39(2):118-124.
［16］	Lowe D G. Distinctive image features from scale-invariant keypoints［J］. International Journal of Computer Vision, 2004,60(2):91-110.
［17］	Fischler M A, Bolles R C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography［J］. Communications of the ACM, 1981,24(6):381-395.〖HJ1.5mm〗
［18］	Amintoosi M, Fathy M, Mozayani N. A fast image registration approach based on SIFT key-points applied to super-resolution［J］. The Imaging Science Journal, 2012,60(4):185-201.
［19］	LIVE Website. Image & Video Quality Assessment at LIVE ［DB/OL］. http://live.ece.utexas.edu/research/quality/, 2012-02-13.

[1]	WU Li-jun, CAI Zhou-wei, CHEN Zhi-cong. Lightweight Super-resolution Networks Based on Improved Residual Feature Distillation [J]. Computer and Modernization, 2022, 0(11): 89-94.
[2]	XIE Hong1，2, LI Zhan1，2, CHEN Hui-qiong1，2, ZHENG Jia-wei1，2, WEN Li1，2. Super Resolution-based Astronomical Image Processing System [J]. Computer and Modernization, 2016, 251(07): 1-5,12.