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Group sparse representation (GSR) has shown great potential in image Compressive Sensing (CS) recovery, which can be considered as a low rank matrix approximation problem. The nuclear norm minimization can only minimize all the singular values simultaneously. Recent advances have suggested the truncated nuclear norm minimization (TNNM) to better approximate the matrix rank. In this paper, we connect...
We proposed a visual saliency detection model for color images based on the reconstruction residual of quaternion sparse model in this paper. This algorithm measures saliency of color image region by the reconstruction residual and performs more consistent with visual perception than current sparse models. In current sparse models, they treat the color images as multiple independent channel images...
Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented,...
In this paper, we propose a quaternion-based sparse representation model for color images and its corresponding dictionary learning algorithm. Differing from traditional sparse image models, which represent RGB channels separately or process RGB channels as a concatenated real vector, the proposed model describes the color image as a quaternion vector matrix, where each color pixel is encoded as a...
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