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An Efficient Selective Perceptual-Based Super-Resolution Estimator
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DOI：10.1109/TIP.2011.2159324.png)
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摘要
En 中文
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.
Keyword:
Edge detection
human visual system (HVS)
maximum a posteriori (MAP) estimator
maximum-likelihood estimator
perceptual quality
reduced complexity
super-resolution (SR)
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期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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