Signal-dependent raw image denoising using sensor noise characterization via multiple acquisitions

Abstract

Accurate noise level estimation is essential to assure good performance of noise reduction filters. Noise contaminating raw images is typically modeled as additive white and Gaussian distributed (AWGN); however raw images are affected by a mixture of noise sources that overlap according to a signal dependent noise model. Hence, the assumption of constant noise level through all the dynamic range represents a simplification that does not allow precise sensor noise characterization and filtering; consequently, local noise standard deviation depends on signal levels measured at each location of the CFA (Color Filter Array) image. This work proposes a method for determining the noise curves that map each CFA signal intensity to its corresponding noise level, without the need of a controlled test environment and specific test patterns. The process consists in analyzing sets of heterogeneous raw CFA images, allowing noise characterization of any image sensor. In addition we show how the estimated noise level curves can be exploited to filter a CFA image, using an adaptive signal dependent Gaussian filter.