Poisson Noise Parameter Estimation and Color Image Denoising for Real Camera Hardware

Date of Award


Degree Name

Ph.D. in Engineering


Department of Electrical and Computer Engineering


Advisor: Keigo Hirakawa


Noise is present in all images captured by real-world image sensors. The distribution of real camera sensor data is well approximated by Poisson, and the estimation of the light intensity signal from the Poisson count data plays a prominent role in digital imaging. Multi-scale Poisson image denoising techniques have processed Haar frame and wavelet coefficients---being enabled by Skellam distribution analysis. Previous work has solved the minimum risk shrinkage operator (MRSO) that produces denoised wavelet coefficients with best achievable Mean Squared Error (MSE) for gray scale image. We extend the idea of MRSO to denoise color sensor data in color-opponent space, improving the quality of denoised color images. In addition, the stable representation of color is to use ratios which we denote by chromaticities. Thus we propose a new Bayes estimator for color image denoising in log-chromaticity coordinate. Using full resolution real R/G/B camera images, we verified that the proposed denoising is more stable than the state-of-art color denoising techniques, yielding higher image quality result. Furthermore, the noise parameters that characterize the level of noise in an image or video frame are required for effective denoising. We develop a novel technique to estimate the noise parameters from natural scenes by exploiting the global joint statistics across multiple video frames, which can be interpreted as a binomial random variable that is insensitive to textures and scene contents. We verify experimentally that the proposed noise parameter estimation method recovers noise parameters more accurately than the state-of-art noise parameter estimation techniques.


Electrical Engineering, Image and video denoising, color image denoising, heteroskedastic noise, Poisson noise, Binomial random variable

Rights Statement

Copyright © 2019, author