IMAGE QUALITY IMPROVEMENT BY COMBINING TRADITIONAL LUCY – RICHARDSON – ROSEN ALGORITHM AND UNET-FORMED DEEP LEARNING TECHNIQUE

Abstract

The contrast, similarity to the original image, and visual quality have been significantly improved with traditional restoration techniques. Among these, the conventional Lucy - Richardson - Rosen - Algorithm is one of the recently created algorithms for boosting resolution and image resemblance to the original. However, tests have found that this method still inserts noise into the recovered image, with artifacts following the image details. This research suggests using additional deep learning approaches to reduce noise and improve the quality of the recovered image. Unet is the deep learning model that was employed. The simulation process results on a set of medical images showed that with a blurred medical image from an optical system, after two processing steps using the traditional Lucy - Richardson - Rosen - Algorithm and combining it with the Unet deep learning network, the reconstructed image was better. The Structural Simililarity Index and Learned Perceptual Image Patch Similarity Index demonstrated that the reconstructed image had lower artifact noise component, better resolution, and a higher degree of resemblace with the original image.