A performance comparison among different super-resolution techniques. (August 2016)

• Record Type: Journal Article
• Authors: Thapa, Damber; Raahemifar, Kaamran; Bobier, William R.; Lakshminarayanan, Vasudevan
• Is Part Of: Computers & electrical engineering. Volume 54(2016)
• Page Start: 313
• Page End: 329
• Publication Date: 2016-08
• Subjects: LR low resolution -- HR high resolution -- SNR Signal-to-Noise Ratio -- SR super-resolution -- PSNR Peak-Signal-to-Noise Ratio -- SSIM Structural Similarity -- MSE Mean Square Error -- EGI Edge-Guided Interpolation -- NEDI New Edge-Directed Interpolation -- GBA Gradient-Based Adaptive -- ASDS Adaptive Sparse Domain Selection -- DFT Discrete Fourier Transform -- DCT Discrete Cosine Transform -- DWT Discrete Wavelet Transform -- IBP Iterative Back Projection -- POCS Projection Onto Convex Set -- MAP Maximum A-Posteriori -- MRF Markov Random Field -- PDF Probability Density Function -- SAR Simultaneous Autoregressive -- CRF Conditional Random Field -- MLE Maximum Likelihood Estimation -- PCA Principle Component Analysis -- CSR Centralized Sparse Representation; Super-resolution -- Reconstruction -- Learning-based -- Example-based -- Sparse representation -- Interpolation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2015.09.011
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Comprehensive survey of super-resolution algorithms. Performance comparison among different super-resolution algorithms. Super-resolution techniques applied to natural images. Abstract: Improving image resolution by refining hardware is usually expensive and/or time consuming. A critical challenge is to optimally balance the trade-off among image resolution, Signal-to-Noise Ratio (SNR), and acquisition time. Super-resolution (SR), an off-line approach for improving image resolution, is free from these trade-offs. Numerous methodologies such as interpolation, frequency domain, regularization, and learning-based approaches have been developed for SR of natural images. In this paper we provide a survey of the existing SR techniques. Various approaches for obtaining a high resolution image from a single and/or multiple low resolution images are discussed. We also compare the performance of various SR methods in terms of Peak SNR (PSNR) and Structural Similarity (SSIM) index between the super-resolved image and the ground truth image. For each method, the computational time is also reported.