Image Processing Algorithms Based On Adaptive Compressed Sensing

Compressed sensing theory is a revolutionary theory of signal processing. In the sampling stage, the efficiency of compression can be effectively improved and storage and transport resources can be saved by sampling adaptively according to the sparsity of different image blocks. So the adaptive sampling method becomes a research hotspot in the field of compressed sensing. Edge information from image edge detection and and the coefficient of discrete cosine transform are important measuring standard of block complexity.When the image is compressed adaptively with compressed sensing theory, the determination of sampling rate and sparsity threshold are highly subjective. In order to solve the problem, an accurately adaptive sampling algorithm with sparsity fitting is proposed in this paper. The minimum sampling rate under certain sparseness is determined to meet the PSNR requirements by iteration, and a optimal objective function of sparsity-sampling rate choices is obtained with the method of least squares fitting sparsity and sampling rate data. The adaptive sampling algorithm is simulated based on TVAL3.Experimental results show that the PSNR values of reconstructed images are higher than that with the same fixed sampling rate algorithm, and the PSNR difference of clear texture distinction images can reach more than 3.5dB. Compared to the roughly adaptive algorithm,when the average sampling rate is lower than that, the reconstructed image obtains a higher PSNR value. In addition, based on the Bessel edge detection algorithm and edge-based adaptive sampling method for multiscale block compressed sensing, we present a more accurate method which uses edge detection method to decide sampling rate adaptively.Compared with the original algorithms, the receiver images obtain better reconstruction quality under the same sampling rate, especially for images with complex texture that also get obvious improvement.