Optimization and Reconstruction for EPMA Image Compressed Sensing Based on Chaotic Measurement Matrix

Image reconstruction is an important part of today's image processing. The quality and efficiency of image re-construction are one of the research hotspots in today's image processing field. Image reconstruction using compressed sensing can greatly reduce the sampling rate and break the constraint of traditional Nyquist sampling law, which is of great breakthrough significance for image reconstruction. The quality of the measurement matrix in compressed sensing has a great influence on the effect of the reconstruction. Therefore, the construction of the measurement matrix is an important direction of the current research on compressed sensing. This paper is using the deterministic Monte Carlo pseudo-random number sampling method to construct a chaotic measurement matrix for compressed sensing. This measurement matrix can solve the uncertainty of the random matrix. The experimental results show that the reconstruction effect of this method on the EPMA image has a better reconstruction performance than other measurement matrices and achieves the super-resolution recovery.