An Efficient Selective Perceptual-Based Super-Resolution Estimator

作者：Karam Lina J^*; Sadaka Nabil G; Ferzli Rony; Ivanovski Zoran A

来源：IEEE Transactions on Image Processing, 2011, 20(12): 3470-3482.

DOI：10.1109/TIP.2011.2159324

• Edge detection
• human visual system (HVS)
• maximum a posteriori (MAP) estimator
• maximum-likelihood estimator
• perceptual quality
• reduced complexity
• super-resolution (SR)

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.

• 出版日期2011-12
• 单位Microsoft