In this work, we propose the first progressive denoising framework inspired from the ALMA framework. In addition, we propose a new metric for evaluating the anytime performance of reconstruction models in the ALMA framework. Furthermore, we analyse and investigate the different effects of changing the scale of progressive noise and the size of the megabatches with respect to the final and the anytime performance.
 Misra, Diganta, Bharat Runwal, Tianlong Chen, Zhangyang Wang, and Irina Rish. “APP: Anytime Progressive Pruning.” arXiv preprint arXiv:2204.01640 (2022).
 Caccia, Lucas, Jing Xu, Myle Ott, Marc’Aurelio Ranzato, and Ludovic Denoyer. “On Anytime Learning at Macroscale.” arXiv preprint arXiv:2106.09563 (2021).
 Zamir, Syed Waqas, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. “Learning enriched features for real image restoration and enhancement.” In European Conference on Computer Vision, pp. 492-511. Springer, Cham, 2020.