Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6883709 | Computers & Electrical Engineering | 2016 | 17 Pages |
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.
Keywords
CRFNEDISSIMPSNREGIDcTPOCSDWTGBASNRIBPSARMLECSRMSEMRFASDsDFTPCAInterpolationReconstructionMaximum likelihood estimationProbability density functionDiscrete Fourier transformDiscrete wavelet transformDiscrete cosine transformPrinciple component analysisconditional random fieldStructural similaritySuper-resolutionMarkov random fieldMean Square ErrorSignal-to-noise ratioPeak-signal-to-noise ratiomapSparse representationLow resolutionPdfhigh resolution
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Networks and Communications
Authors
Damber Thapa, Kaamran Raahemifar, William R. Bobier, Vasudevan Lakshminarayanan,