An improved raw image enhancement algorithm using a statistical model for pixel value error

Abstract

When an image is captured using an electronic sensor, statistical variations introduced by photon shot and other noise introduce errors in the raw value reported for each pixel sample. Earlier work found that modest improvements in raw image data quality reliably could be obtained by using empirically-determined pixel value error bounds to constrain texture synthesis. However, the prototype software implementation, KREMY (KentuckY Raw Error Modeler, pronounced “creamy”), was not effective in processing very noisy images. In comparison, the current work has reimplemented KREMY to make it capable of credibly improving far noisier raw DNG images. The key is a new approach that uses a simpler, but statistical, model for pixel value errors rather than simple bounds constraints.