RECURSIVE CONDITIONAL MEANS IMAGE DENOISING

摘要

Methods and composition for denoising digital camera images are provided herein. The method is based on directly measuring the local statistical structure of natural images in a large training set that has been corrupted with noise mimicking digital camera noise. The measured statistics are conditional means of the ground truth pixel value given a local context of input pixels. Each conditional mean is the Bayes optimal (minimum mean squared error) estimate given the specific local context. The conditional means are measured and applied recursively (e.g., the second conditional mean is measured after denoising with the first conditional mean). Each local context vector consists of only three variables, and hence the conditional means can be measured directly without prior assumptions about the underlying probability distributions, and they can be stored in fixed lookup tables.