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.
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