A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and robust multiframe super resolution
IEEE Transactions on Image Processing
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An efficient Perceptually Attentive (PA) Super-Resolution method is proposed to significantly reduce the computational complexity of iterative super-resolution algorithms without loss of the desired perceptual quality. A perceptually significant constrained set of active pixels is selected for processing by the SR algorithm based on a just noticeable distortion threshold model. These selected active pixels are further reduced by using saliency information that is determined by a visual attention model. Furthermore, the active pixels lying in the attended regions are processed at a higher accuracy by the SR method relative to pixels in other regions. Simulation results are presented to show the preserved desired visual quality and a 30-40% reduction in complexity over a highly efficient Selective Perceptual Fast Two-Step (SELP-FTS) scheme.