Image noise parameter estimation by principal component analysis (Q2863747)

By the author, a new framework for noise parameter estimation is proposed in this dissertation, which contains algorithms for processing signal-independent noise as well as signal-dependent noise. Principal component analysis (PCA) of image blocks is utilized for signal and noise separation. First, image blocks of a fixed size are generated from the input image in a sliding window manner. Then, these blocks are rearranged into vectors and PCA of these vectors is done. It is assumed that the last principal component is not affected by the signal and corresponds only to the noise, so that one canNEWLINENEWLINE1. In the case of signal-independent noise estimate the noise variance as the variance of the last principal component.NEWLINENEWLINE 2. in the case of signal-dependent noise assess noise normality by analyzing the distribution of the last principal component, and select the variance-stabilizing transformation parameters in order to transform the noise into an additive white Gaussian noise.NEWLINENEWLINE This dissertation contains 6 chapters and an appendix. Chapter 1 describes most common noise sources and noise types. For each noise type, the noise distribution is derived using the imaging system properties. Chapter 2 considers existing noise parameter estimation algorithms. Since many of them have common ideas, the algorithms are grouped by the assumption about the input image and by the approach. In Chapter 3, the proposed white noise variance estimation method is explained. First, the necessary mathematical framework is developed and analyzed. Next, the algorithm is presented and evaluated. The extension to signal-dependent noise is given in Chapter 4, which contains a description of VSTs and noise normality assessment methods as well as the proposed algorithm and the evaluation. The denoising experiments presented in Chapter 5 show how the usage of noise parameters computed by different methods affects the denoising quality. These experiments confirm the applicability of the proposed noise parameter estimation framework. The conclusion is given in Chapter 6. The proof of Theorem 1 is provided in the appendix.