NON-LOCAL MEANS IMAGE DENOISING WITH DETAIL PRESERVATION USING SELF-SIMILARITY DRIVEN BLENDING

摘要

System, apparatus, method, and computer readable media for texture enhanced non-local means (NLM) image denoising. In embodiments, detail is preserved in filtered image data through a blending between the noisy input target pixel value and the NLM pixel value that is driven by self-similarity and further informed by an independent measure of local texture. In embodiments, the blending is driven by one or more blending weight or coefficient that is indicative of texture so that the level of detail preserved by the enhanced noise reduction filter scales with the amount of texture. Embodiments herein may thereby denoise regions of an image that lack significant texture (i.e. are smooth) more aggressively than more highly textured regions. In further embodiments, the blending coefficient is further determined based on similarity scores of candidate patches with the number of those scores considered being based on the texture score.

主权项

1. A computer-implemented image noise reduction method, comprising:
receiving input pixel values for a target pixel and a plurality of pixels within a spatial neighborhood of the target pixel; computing a texture score indicative of a level of texture within the neighborhood based on the input pixel values; computing a non-local mean of the target input pixel based on the input pixel values; modulating a weighting of the non-local mean relative to the target input pixel value inversely with the level of texture by computing a filtered target pixel value that is a blend of the non-local mean and the target input pixel value based on the texture score; and storing the filtered target pixel value to an electronic memory.