Abstract
In this paper, a selective perceptual-based (SELP) framework is presented to reduce the complexity of popular super-resolution (SR) algorithms while maintaining the desired quality of the enhanced images/video. A perceptual human visual system model is proposed to compute local contrast sensitivity thresholds. The obtained thresholds are used to select which pixels are super-resolved based on the perceived visibility of local edges. Processing only a set of perceptually significant pixels reduces significantly the computational complexity of SR algorithms without losing the achievable visual quality. The proposed SELP framework is integrated into a maximum-a posteriori-based SR algorithm as well as a fast two-stage fusion-restoration SR estimator. Simulation results show a significant reduction on average in computational complexity with comparable signal-to-noise ratio gains and visual quality.
Original language | English (US) |
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Article number | 5873150 |
Pages (from-to) | 3470-3482 |
Number of pages | 13 |
Journal | IEEE Transactions on Image Processing |
Volume | 20 |
Issue number | 12 |
DOIs | |
State | Published - Dec 2011 |
Keywords
- Edge detection
- Human visual system (HVS)
- Maximum a posteriori (MAP) estimator
- Maximum-likelihood estimator
- Perceptual quality
- Reduced complexity
- Super-resolution (SR)
ASJC Scopus subject areas
- Software
- Computer Graphics and Computer-Aided Design