Imagery Overlap Block Compressive Sensing With Convex Optimization

To improve reconstruction performance in imagery compressive sensing, the present paper changes solving a block image compressive sensing reconstruction into a convex optimization problem. First, a Total-Variation norm minimization constraints model that includes both L1 and L2 norm functions is established. The split Bregman iterative method solves the model with convex optimization. Then, a robust adaptive image block compressive sensing algorithm is studied based on an analysis of the image features. The image is divided into blocks, and an overlap image block compressive reconstruction method is proposed. Finally, to solve the block effect caused by block compressive sensing reconstruction, a novel image overlap block compressive sensing reconstruction based on the Poisson function is suggested to avoid the block effect in the reconstruction process. The experimental results show that compared with other traditional compressive sensing reconstruction algorithms, the proposed method can generate a better image reconstruction result. According to the PSNR evaluation, when the sampling rate is 0.3, the proposed method is improved by more than 20.98% compared to the conventional techniques, and according to the SSIM evaluation, it has improved by more than 11.92% from the traditional methods. We can also find that the proposed method has better construction effect for traffic sign image recognition compared with ordinary natural image reconstruction. When the sampling rate is only 0.1, the PSNR value reaches 44.28dB, and the SSIM reconstruction accuracy reaches 98.14%. After reconstructing different types and characteristic images, it is supported that the proposed algorithm has good robustness and anti-noise performance.