Image Denoising: A Review about its Past, Present, and Future
Image Denoising: A Review about its Past, Present, and Future |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-11 |
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| Year of Publication : 2024 | ||
| Author : Ana Cláudia Souza Vidal de Negreiros, Gilson Giraldi |
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| DOI : 10.14445/22315381/IJETT-V72I11P121 | ||
How to Cite?
Ana Cláudia Souza Vidal de Negreiros, Gilson Giraldi, "Image Denoising: A Review about its Past, Present, and Future," International Journal of Engineering Trends and Technology, vol. 72, no. 11, pp. 192-206, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I11P121
Abstract
Digital images have played an important role in the modern world. In this sense, image noise reduction techniques have become an important research field for many reasons. This work presents a whole perspective of the image denoising field since the first published article to state-of-the-art research. In this context, we analyzed classical and modern methods and metrics applied to remove noise from images, considering the whole collection encountered in the SCOPUS database. It was verified that classical computer vision techniques were extensively used in this context, above all different image filters. On the other hand, modern techniques hold on deep learning models, including combining them with classical techniques. Models such as Generative Adversarial Networks (GANs), variational techniques, residual networks, denoising diffusion probabilistic models, and quantum computing appear as future trends in this knowledge area. Also, some research gaps were found.
Keywords
Classical computer vision techniques, Deep learning models, Image denoising, SCOPUS database, GANs.
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