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Full-Text Articles in Engineering
Numerical Algorithms For Solving Nonsmooth Optimization Problems And Applications To Image Reconstructions, Karina Rodriguez
Numerical Algorithms For Solving Nonsmooth Optimization Problems And Applications To Image Reconstructions, Karina Rodriguez
REU Final Reports
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.
No-Reference Image Denoising Quality Assessment, Si Lu
No-Reference Image Denoising Quality Assessment, Si Lu
Computer Science Faculty Publications and Presentations
A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a noreference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and …
Sparse Adaptive Local Machine Learning Algorithms For Sensing And Analytics, Jack Cannon
Sparse Adaptive Local Machine Learning Algorithms For Sensing And Analytics, Jack Cannon
Undergraduate Research & Mentoring Program
The goal of digital image processing is to capture, transmit, and display images as efficiently as possible. Such tasks are computationally intensive because an image is digitally represented by large amounts of data. It is possible to render an image by reconstructing it with a subset of the most relevant data. One such procedure used to accomplish this task is commonly referred to as sparse coding. For our purpose, we use images of handwritten digits that are presented to an artificial neural network. The network implements Rozell's locally competitive algorithm (LCA) to generate a sparse code. This sparse code is …