基于压缩感知的图像快速重构去噪算法

针对传统的压缩感知重构算法运算量大,图像质量低的缺点,提出一种新的图像快速重构去噪算法。首先对图像进行一级小波分解,分别提取近似分量子图像和细节分量子图像,并对细节分量子图像进行软阈值去噪处理,然后对近似分量子图像和处理后的细节分量子图像运用新的压缩感知重构算法进行恢复,最后将恢复的细节分量和近似分量进行小波逆变换,得到重构后的图像。实验结果表明,新方法可减少重构时的运算量,有一定的去噪效果,且可提高图像质量。

Fast reconstruction denoising algorithm of gray image based on compressive sensing

Due to the disadvantage of large computation and image quality degradation of classical reconstruction algorithm, a novel image fast reconstruction denoising algorithm based on traditional compression sensing is presented. Firstly, A l-layer wavelet decomposition is used to extract the approximate coefficients and detail coefficients from the image and a soft threshold denoising method is used to deal with extracted detail coefficients. Secondly, a new compression sensing reconstruction method has been used to recover these approximate coefficients and disposaled detail coefficients. Finally, the reconstructed images are obtained based on recovered detail coefficients and approximate coefficients by wavelet inversed-transform. Experimental results demonstrate that the proposed method can reduce the computation, remove image noise and improve the quality of the reconstructed image.