Poissonian Blurred Image Deconvolution by Framelet based Local Minimal Prior

TL;DR

This paper introduces a novel image deconvolution method using framelet-based local minimal prior and fractional calculus to enhance Poissonian noisy and blurry images, especially in medical and astronomical contexts.

Contribution

It proposes a new deconvolution model combining framelet transform, local minimal prior, and fractional calculation, generalized to blind deconvolution for Poissonian noise.

Findings

Effective in restoring medical and astronomical images

Improves image clarity under Poissonian noise and blur

Validated on real images with promising results

Abstract

Image production tools do not always create a clear image, noisy and blurry images are sometimes created. Among these cases, Poissonian noise is one of the most famous noises that appear in medical images and images taken in astronomy. Blurred image with Poissonian noise obscures important details that are of great importance in medicine or astronomy. Therefore, studying and increasing the quality of images that are affected by this type of noise is always considered by researchers. In this paper, in the first step, based on framelet transform, a local minimal prior is introduced, and in the next step, this tool together with fractional calculation is used for Poissonian blurred image deconvolution. In the following, the model is generalized to the blind case. To evaluate the performance of the presented model, several images such as real images have been investigated.