Research on Image Processing Algorithms Based on Variational and Deep Learning

China has seen an unheard-of surge in interest in deep-learning methods for image restoration in recent years. Most of these strategies draw inspiration from the established variational technique and related optimization methods for the picture reconstruction inverse issue. While using learnable com...

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Bibliographic Details
Published in:	2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII) pp. 317 - 321
Main Author:	Zhang, Xiaoxu
Format:	Conference Proceeding
Language:	English
Published:	IEEE 12.06.2024
Subjects:	Deep learning Deep Learning Networks Image Compression Algorithms Image Reconstruction Optimization methods Optimized Systems Protocols Retina Sensitivity Training Variational Models Visual systems
Online Access:	Get full text
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Summary:	China has seen an unheard-of surge in interest in deep-learning methods for image restoration in recent years. Most of these strategies draw inspiration from the established variational technique and related optimization methods for the picture reconstruction inverse issue. While using learnable components to create organized deep neural networks and using copious amounts of observation data to train the networks for the particular reconstruction objectives, these techniques resemble the iterative strategies of ordinary optimization algorithms. In many cases, they have proven to have far better empirical performance than the conventional approaches, and they also demand a lot lower computing cost. For various common networks in this subject, this research offers the specifics of the derivations, the network topologies, and the training protocol. The research therefore focuses on image processing algorithms based on variational and deep learning models.
DOI:	10.1109/ICMIII62623.2024.00064