Abstract: Compressed sensing has been developed for many years, and there are many reconstruction algorithms. The stagewise weak orthogonal matching pursuit (SWOMP) algorithm, which does not require sparsity, is an improved algorithm. The measurement matrix selects Gaussian matrix, but its reconstruction effect is not ideal. Aiming at the shortcomings of the algorithm, the algorithm is optimized by combining the electron probe image. This optimization takes full advantage of the Fourier matrix and adjusts the number of iterations and threshold parameters. Firstly, the commonly used matrix is tested several times to find the best quality measurement matrix—Fourier orthogonal matrix. Secondly, the iteration number and threshold are modified to find the best parameter matching to improve the reconstruction quality of the algorithm. The experimental results show that the proposed method has better reconstruction effect on the electron probe image and achieves the super-resolution recovery requirement. The reconstructed image quality is higher than the original one.

Key words: image processing, compressed sensing, electron probe, super-resolution reconstruction, orthogonal matching pursuit