High-resolution optical image reconstruction based on adaptive sparse dictionary

Image spatial resolution reflects the ability of an optical system to capture detailed information about an object. Compared to Low-resolution (LR) images, High-resolution (HR) images contain greater pixel density and textural detail. In the experiments, it is difficult to obtain ideal HR images due to the effects of acquisition equipment and image degradation. To address the problems, this paper proposes a HR optical image reconstruction method based on adaptive sparse dictionary. The experimental show that the area of the modulation transfer function (MTF) curve of the reconstructed image is improved by 16.41%, which represents the increase in the frequency components it contains. Meanwhile the cut-off resolution is improved from 0.2692cy/pix to 0.4018cy/pix. The method in paper achieves good results in both reconstruction efficiency and accuracy. The peak signal-to-noise ratio (PSNR) of the reconstructed image is improved from 20.1650 to 28.0192. The feature similarity (FSIM) and detail retention are above 90%.