An Efficient Selective Perceptual-Based Super-Resolution Estimator
An Efficient Selective Perceptual-Based Super-Resolution Estimator
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DOI：
10.1109/tip.2011.2159324
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发表时间：
2011-12
影响因子：
10.6
通讯作者：
Lina Karam;Nabil G. Sadaka;R. Ferzli;Z. Ivanovski
中科院分区：
文献类型：
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作者：
Lina Karam;Nabil G. Sadaka;R. Ferzli;Z. Ivanovski
在本文中，提出了一种基于选择性感知（SELP）的框架，以降低流行的超分辨率（SR）算法的复杂性，同时保持增强图像/视频的所需质量。提出了一种感知人类视觉系统模型来计算局部对比敏感度阈值。获得的阈值用于根据局部边缘的感知可见性来选择哪些像素是超分辨率的。仅处理一组感知上重要的像素可显着降低 SR 算法的计算复杂性，而不会损失可实现的视觉质量。所提出的 SELP 框架被集成到基于最大后验的 SR 算法以及快速两阶段融合恢复 SR 估计器中。仿真结果表明，计算复杂性平均显着降低，信噪比增益和视觉质量也相当。
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.