Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing

Folberth, James

Graduate Thesis Or Dissertation

Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing Public Deposited

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Citeable URL: https://scholar.colorado.edu/concern/graduate_thesis_or_dissertations/cr56n098w

Abstract

In this dissertation we consider numerical methods for a problem in each of numerical linear algebra, digital signal processing, and image processing for super-resolution fluorescence microscopy. We consider first a fast, randomized mixing operation applied to the unpivoted Householder QR factorization. The method is an adaptation of a slower randomized operation that is known to provide a rank-revealing factorization with high probability. We perform a number of experiments to highlight possible uses of our method and give evidence that our algorithm likely also provides a rank-revealing factorization with high probability.

In the next chapter we develop fast algorithms for computing the discrete, narrowband cross-ambiguity function (CAF) on a downsampled grid of delay values for the purpose of quickly detecting the location of peaks in the CAF surface. Due to the likelihood of missing a narrow peak on a downsampled grid of delay values, we propose methods to make our algorithms robust against missing peaks. To identify peak locations to high accuracy, we propose a two-step approach: first identify a coarse peak location using one of our delay-decimated CAF algorithms, then compute the CAF on a fine, but very small, grid around the peak to find its precise location. Runtime experiments with our C++ implementations show that our delay-decimated algorithms can give more than an order of magnitude improvement in overall runtime to detect peaks in the CAF surface when compared against standard CAF algorithms.

In the final chapter we study non-negative least-squares (NNLS) problems arising from a new technique in super-resolution fluorescence microscopy. The image formation task involves solving many tens of thousands of NNLS problems, each using the same matrix, but different right-hand sides. We take advantage of this special structure by adapting an optimal first-order method to efficiently solve many NNLS problems simultaneously. Our NNLS problems are extremely ill-conditioned, so we also experiment with using a block-diagonal preconditioner and the alternating direction method of multipliers (ADMM) to improve convergence speed. We also develop a safe feature elimination strategy for general NNLS problems. It eliminates features only when they are guaranteed to have weight zero at an optimal point. Our strategy is inspired by recent works in the literature for ℓ₁-regularized least-squares, but a notable exception is that we develop our method to use an inexact, but feasible, primal-dual point pair. This allows us to use feature elimination reliably on the extremely ill-conditioned NNLS problems from our microscopy application. For an example image reconstruction, we use our feature elimination strategy to certify that the reconstructed super-resolved image is unique.

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