Super-Resolution Image Optimisation Based on Gradient Iterative Fast Diffraction-Free Spot Algorithm - Chen Yu; Ying Liu; Linhan Li; Guangpeng Zhou; Boshi Dang; Jie Du; Junlin Ma; Site Zhang (Sensors)
Diffraction significantly deteriorates the quality of the laser image, causing severe degradation that undermines the theoretical performance parameters of the autofocus system. In this paper, we conduct a comprehensive analysis of the non-uniform features of the images. To enhance the imaging quality of each individual image, we propose a de-diffraction algorithm based on gradient iteration. This algorithm is capable of rapidly removing the interference spots resulting from diffraction and restoring the distorted laser spots. By doing so, it effectively eliminates the inevitable reduction in the autofocus resolution and focusing accuracy caused by diffraction. Furthermore, the proposed calculation model for the intra-localisation interval significantly improves the convergence of the iterative calculation process. Through experiments, it has been verified that, under the same conditions, the interlayer resolution between the reflective surfaces of the samples processed using this algorithm is increased to a quarter of the original value. This remarkable improvement in resolution, which far exceeds the microscope’s inherent resolution, demonstrates that the algorithm successfully achieves super-resolution for the microscope.