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A performance comparison among different super-resolution techniques
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DOI：10.1016/j.compeleceng.2015.09.011.png)
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Abstract
En 中文
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. (C) 2015 Elsevier Ltd. All rights reserved.
Keywords:
Super-resolution
Reconstruction
Learning-based
Example-based
Sparse representation
Interpolation
Journal
C
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