基于相异性阈值的改进自适应稀疏表示去噪算法

Modified adaptive sparse representation denoising algorithm based on difference threshold

针对自适应稀疏表示去噪算法在对图像进行去噪时运行时间较长,得到结果过于平滑的问题,研究了基于相异性阈值的改进自适应稀疏表示去噪算法,在改进算法中,计算当前提取的图像块与前一个图像块之间的相异性度量,并与阈值进行比较,低于阈值则认为两者具有相同的稀疏表示向量和表示误差,不需要对当前块再执行计算从而减少运行时间,高于阈值则认为当前块包含了边缘区域,记录其位置,在重构去噪图像时予以保护,以减少图像边缘信息的损失.对毫米波图像的去噪实验结果证实了改进算法的有效性.

In order to decrease the time and alleviate the smoothness of the adaptive sparse representation algorithm,a modified adaptive sparse representation algorithm based on difference threshold is introduced in this paper. This modified algorithm computes the difference between the current block and the previous one,then compares the difference with the threshold. When the difference is less than the threshold,the two blocks are considered having the same sparse representation vector and error. It is not needed to compute over current block again. When the difference is greater,they are considered as different. The current block contains the edge area and its position is recorded. It is then protected from averaging in reconstructing the result to alleviate the smoothness. The experimental results performed on millimeter-wave image demonstrated the effectiveness of the proposed method.