Uncertainty Estimation for Super-Resolution using ESRGAN

Autor:	Adapa, Maniraj Sai, Zullich, Marco, Valdenegro-Toro, Matias
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Deep Learning-based image super-resolution (SR) has been gaining traction with the aid of Generative Adversarial Networks. Models like SRGAN and ESRGAN are constantly ranked between the best image SR tools. However, they lack principled ways for estimating predictive uncertainty. In the present work, we enhance these models using Monte Carlo-Dropout and Deep Ensemble, allowing the computation of predictive uncertainty. When coupled with a prediction, uncertainty estimates can provide more information to the model users, highlighting pixels where the SR output might be uncertain, hence potentially inaccurate, if these estimates were to be reliable. Our findings suggest that these uncertainty estimates are decently calibrated and can hence fulfill this goal, while providing no performance drop with respect to the corresponding models without uncertainty estimation. Comment: 8 pages, 6 figures. VISAPP 2025 camera ready
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2412.15439 Zobrazit plný text záznamu View this record from Arxiv