Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy

Boosting Noise Reduction Effect via Unsupervised Fine-Tuning Strategy

Over the last decade, supervised denoising models, trained on extensive datasets, have exhibited remarkable performance in image denoising, owing to their superior denoising effects. However, these models exhibit limited flexibility and manifest varying degrees of degradation in noise reduction capa...

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Bibliographic Details
Main Authors: Xinyi Jiang, Shaoping Xu, Junyun Wu, Changfei Zhou, Shuichen Ji
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Applied Sciences
Subjects:
boosting denoising effect
supervised denoising models
data bias
unsupervised denoising models
flexibility
fine-tuning
Online Access:https://www.mdpi.com/2076-3417/14/5/1742

Internet

https://www.mdpi.com/2076-3417/14/5/1742

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