Structured compressed sensing image reconstruction based on double-density dual-tree complex wavelet transform

We propose a new structured compressed sensing recovery algorithm of images based on double-density dual-tree complex wavelet transform (DDDT-CWT). The algorithm combines the structured characteristic of coefficients under DDDT-CWT and compressive sample matching pursuit (CoSaMP) recovery algorithm. It has good reconstructed image performance. Simulation results show advantages of the proposed method as compared with traditional recovery algorithm using DWT basis and without considering structured characteristic of coefficients. With the same compression ratio, PSNR is improved by 2.9~3.2 dB and 0.2~1.2 dB when using the DDDT-CWT basis and considering structured characteristic respectively. The PSNR gain reaches 3.8~4.3 dB when combining these two features together.