Edge-preserving adaptive autoregressive model for Poisson noise reduction

Abstract

Autoregressive models in image processing are linear prediction models that split an image into a predicted (i.e. filtered) image and a prediction error image, which extracts data on the image edges. Edge separation is a crucial feature of an autoregressive model. Data on the edges can be processed in different ways and then added to the filtered image. Another basic feature of our method is spatially varying modelling. In this short article, we propose an improved autoregressive model that preserves image sharpness around the edges of the image and focus on the reduction of Poisson noise, which degrades nuclear medicine images and presents a special challenge in medical imaging.