Date of Award
6-9-2024
Document Type
Thesis
Publisher
Santa Clara : Santa Clara University, 2024
Degree Name
Master of Science (MS)
Department
Computer Science and Engineering
First Advisor
Nam Ling
Abstract
In the field of image reconstruction and super-resolution, using codebooks has shown promising results despite various image degradations. Previous methods either use distinct codebooks for each image category or multiple codebooks per category, with the latter achieving better performance by capturing more nuanced image features. Our research proposes a novel method that employs enhanced sets of codebooks and weight maps tailored to each image category. These weight maps dynamically combine different codebook bases to adapt to various reconstruction tasks, resulting in improved image recognition and robustness. This approach significantly enhances the expressiveness and quality of reconstructed images, making it versatile and effective for handling diverse image degradation scenarios.
Recommended Citation
Ge, Yutong, "Enhanced Adaptive Image-Codebook Learning for Image Reconstruction" (2024). Computer Science and Engineering Master's Theses. 41.
https://scholarcommons.scu.edu/cseng_mstr/41