自适应小波包图像压缩感知方法

Adaptive Wavelet Packet Image Compressed Sensing

该文提出一种自适应小波包图像压缩感知方法。该方法选用小波包变换分解图像,基于数学期望和信息熵分析各个小波包系数块的属性,自适应地将其划分为低频信号、无价值信号、特殊处理信号和压缩感知处理信号等4种信号类型,再针对不同的信号类型设计对应的处理方法,适应不同特征的图像。通过此种方法,在图像压缩感知过程中,可以根据不同图像和小波包系数块自适应地选取采样值,来提高压缩感知质量。实验结果表明该文提出的自适应小波包图像压缩感知方法在相同采样值的前提下,不仅提高了图像的重构质量,同时也降低了算法的计算复杂度和所需存储空间。

An adaptive wavelet packet image compressed sensing is proposed, in which the wavelet packet transform is used to decompose the image. After the image is decomposed, the properties of each packet wavelet block are analyzed with the introduction of mathematical expectation and information entropy. According to the characteristic of each packet wavelet block, the signals are classified to four types of signal, that is the low frequency signal, no value signal, special processing signal and compressed sensing processing signal adaptively. Then the corresponding methods are designed to deal with different types of signal, which can adapt to the different characteristic of images. In this method, the quality of compressed sensing is improved, which is because sampling numbers can be adaptively selected according to different images and packet wavelet blocks. Experimental results show that, when the sampling number is the same, the proposed algorithm can not only greatly improve the reconstruction quality of image, but also reduce the computational complexity and required memory.