基于小波变换的图像零树压缩感知方法

稀疏性是压缩感知的前提,然而,自然图像通常不是稀疏的,因此对图像直接应用压缩感知算法很难取得高压缩效率.针对图像信号,将编码思想融入压缩感知理论,提出一种简单有效的零树压缩感知方法.该方法先利用零树思想辅助压缩感知测量,在得到测量值的同时编码重要系数的位置;然后提出零树追踪重构算法,通过精确解码重要系数位置来重构原始图像小波系数,提高重构精度.实验结果表明,相比于现有匹配追踪算法和EZW算法,本文方法有更高的压缩比和更好的图像重构质量.

Image Zerotree Compressed Sensing Based on Wavelet Transform

The basic principle of Compressed Sensing (CS) theory is that if a signal is sparse, CS promises to deliver a full recovery of this signal with high probability from far fewer measurements than the original signal. Unfortunately, image signals usually are not sparse, and thus it is difficult to obtain high compression performance for image compressed sensing.This paper proposed a simple and efficient zerotree compressed sensing method for images. In the proposed scheme, the classical zerotree coding is integrated into the process of measure to encode the precise locations of significant elements, which is used to restore the original image by the proposed pursuit reconstruction algorithm to improve the quality of the reconstructed image. The experimental results show that, compared with the existing matching pursuit algorithms and Embedded Zerotree Wavelet (EZW) coding algorithm, the proposed algorithm achieves much higher compression ratio and better image quality.