A performance comparison among different super-resolution techniques

作者：Thapa Damber; Raahemifar Kaamran^*; Bobier William R; Lakshminarayanan Vasudevan

来源：Computers & Electrical Engineering, 2016, 54: 313-329.

DOI：10.1016/j.compeleceng.2015.09.011

• Super-resolution
• Reconstruction
• Learning-based
• Example-based
• Sparse representation
• Interpolation

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.

• 出版日期2016-8