Advisor

Mau Nam Nguyen

Document Type

Report

Publication Date

8-23-2019

Subjects

Mathematical optimization -- Algorithms, Image reconstruction, Random noise theory, Smoothing (Numerical analysis)

Abstract

In this project, we apply nonconvex optimization techniques to study the problems of image recovery and dictionary learning. The main focus is on reconstructing a digital image in which several pixels are lost and/or corrupted by Gaussian noise. We solve the problem using an optimization model involving a sparsity-inducing regularization represented as a difference of two convex functions. Then we apply different optimization techniques for minimizing differences of convex functions to tackle the research problem.

Persistent Identifier

https://archives.pdx.edu/ds/psu/29451

Annotated_Bibliography.docx (273 kB)
Annotations of 5 papers

CryoEM using MicroED Method.pptx (2533 kB)
Summary of research paper and exercise

symposium_presentation.pdf (1251 kB)
Final presentation

Available for download on Wednesday, December 23, 2020

Share

COinS