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
Citation Details
Rodriguez, Karina, "Numerical Algorithms for Solving Nonsmooth Optimization Problems and Applications to Image Reconstructions" (2019). REU Final Reports. 10.
https://pdxscholar.library.pdx.edu/reu_reports/10
Annotations of 5 papers
CryoEM using MicroED Method.pptx (2533 kB)
Summary of research paper and exercise
symposium_presentation.pdf (1251 kB)
Final presentation