Abstract:

Reconstruction of full image of large size by applying compressed sensing theory requires a long period of time， the observation matrix of linear measurement requires large space，and there is obvious block effect in the reconstructed image.According to the characteristics of the wavelet transformation coefficients of the image，the image block is combined with the DWT transformation,and an improved DWT based image compressed algorithm is proposed.After the DWT transformation，the low frequency coefficients of each image block are preserved，and only the high frequency coefficients are observed.The high frequency coefficients are recovered by the orthogonal matching pursuit algorithm (OMP).Matlab simulation experiment shows that, compared with the non-block compressed sensing algorithm, the algorithm proposed increases the PSNR value of the reconstructed image by  2~4 dB and decreases the reconstructing time significantly; and compared with the two-dimensional discrete cosine transformation (DCT) block compressed sensing algorithm，this algorithm decreases the block effect and improves reconstruction quality significantly.

Key words: compressed sensing, image blocking, sampling rate, peak signal to noise ratio