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Statistical Approaches to Color Image Denoising and Enhancement

Miller, Sarah Victoria
http://orcid.org/0000-0001-7593-8624
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1682843329698086

Abstract Details

2023, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical 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.
Keigo Hirakawa (Advisor)
Raul Ordonez (Committee Member)
Kenneth Barnard (Committee Member)
Stanley Chan (Committee Member)
Bradley Ratliff (Committee Member)
74 p.
Electrical Engineering; Engineering; Statistics
image denoising; Bayesian; Noise2Noise; Aitchison; image enhancement; composite material; X-ray/CT

Recommended Citations

Citations

  • Miller, S. V. (2023). Statistical Approaches to Color Image Denoising and Enhancement [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1682843329698086

    APA Style (7th edition)

  • Miller, Sarah. Statistical Approaches to Color Image Denoising and Enhancement. 2023. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1682843329698086.

    MLA Style (8th edition)

  • Miller, Sarah. "Statistical Approaches to Color Image Denoising and Enhancement." Doctoral dissertation, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1682843329698086

    Chicago Manual of Style (17th edition)

dayton1682843329698086
84
© 2023, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.