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arXiv · 2111.12350

Supervised Neural Discrete Universal Denoiser for Adaptive Denoising

Sungmin Cha·Seonwoo Min·Sungroh Yoon·Taesup Moon
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Abstract

We improve the recently developed Neural DUDE, a neural network-based adaptive discrete denoiser, by combining it with the supervised learning framework. Namely, we make the supervised pre-training of Neural DUDE compatible with the adaptive fine-tuning of the parameters based on the given noisy data subject to denoising. As a result, we achieve a significant denoising performance boost compared to the vanilla Neural DUDE, which only carries out the adaptive fine-tuning step with randomly initialized parameters. Moreover, we show the adaptive fine-tuning makes the algorithm robust such that a noise-mismatched or blindly trained supervised model can still achieve the performance of that of the matched model. Furthermore, we make a few algorithmic advancements to make Neural DUDE more scalable and deal with multi-dimensional data or data with larger alphabet size. We systematically show our improvements on two very diverse datasets, binary images and DNA sequences.

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Sungmin Cha, Seonwoo Min, Sungroh Yoon, Taesup Moon. 2021-11-24. Supervised Neural Discrete Universal Denoiser for Adaptive Denoising. https://arxiv.org/abs/2111.12350

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