Mau Nam Nguyen
Mathematical optimization -- Algorithms, Image reconstruction, Random noise theory, Smoothing (Numerical analysis)
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.
Rodriguez, Karina, "Numerical Algorithms for Solving Nonsmooth Optimization Problems and Applications to Image Reconstructions" (2019). REU Final Reports. 10.
Available for download on Wednesday, December 23, 2020