Statistical Approaches to Color Image Denoising and Enhancement
Date of Award
Ph.D. in Electrical and Computer Engineering
Department of Electrical and Computer Engineering
This dissertation is comprised of two novel contributions. First, we propose a novel technique to determine the noise-free color at each pixel by estimating the ratio of the red, green, and blue (RGB) pixel values from their noisy version. In order to model the spatial statistics of the proportion of primary colors such as RGB components known to correspond to the human perception of color, we interpret the simplex representation of color as an Aitchison geometry. Specifically, we develop a minimum mean square error (MMSE) estimator of log-color pixel values in the wavelet representation, with Poisson as its pixel domain likelihood function. We contrast this to most existing denoising techniques that are predominantly designed for single-channel/greyscale images that are then applied to YCbCr channels independently without regard for the RGB proportionality. In the extremely low photon regime, we verify experimentally that the proposed method yields state-of-the-art color denoising performance. Second, we propose a novel image enhancement algorithm to assist with the automation of the quantification and characterization of fiber reinforced composite materials. The success of this Aitchison- and Noise2Noise-based enhancement algorithm allows for faster and more accurate classification of composite materials that are frequently used in aerospace systems. The enhancement algorithm is applied to X-ray/CT scans of composite materials and the resulting denoised frames are classified utilizing DRAGONFLY technology. It is found that the enhanced images are able to achieve superior classification accuracy as compared to unprocessed images.
Electrical Engineering, Engineering, Statistics, image denoising, Bayesian, Noise2Noise, Aitchison, image enhancement, composite material, X-ray/CT
Copyright 2023, author
Miller, Sarah Victoria, "Statistical Approaches to Color Image Denoising and Enhancement" (2023). Graduate Theses and Dissertations. 7241.