The 2021 IEEE Winter Conference on Applications of Computer Vision (WACV 2021) on Wednesday announced that a paper co-authored by LLNL computer scientist Rushil Anirudh received the conference’s Best Paper Honorable Mention award based on its potential impact to the field. The paper, titled "Generative Patch Priors for Practical Compressive Image Recovery,” introduces a new kind of prior—a characterization of the space of natural images—for compressive image recovery that is trained on patches of images instead of full-sized images. “The central idea behind GPP is to exploit the fact that images can be broken down into small patches, and it is much easier for a machine learning model to approximate all the variations in patches than in images due to the reduced size,” explained lead author Anirudh. Read more at LLNL News.
Jeremy Thomas/LLNL
