Poissonian Blurred Image Deconvolution by Framelet based Local Minimal Prior
This addresses the problem of improving image quality in medical and astronomy images affected by Poissonian noise and blur, but it appears incremental as it builds on existing framelet and fractional methods.
The paper tackled Poissonian blurred image deconvolution by introducing a local minimal prior based on framelet transform and using it with fractional calculation, and generalized the model to the blind case. The result was evaluated on several real images, but no concrete numbers were provided.
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.