Unsupervised Image Denoising with Frequency Domain Knowledge

• URL: http://arxiv.org/abs/2111.14362v1
• Date: Mon, 29 Nov 2021 07:41:32 GMT
• Title: Unsupervised Image Denoising with Frequency Domain Knowledge
• Authors: Nahyun Kim, Donggon Jang, Sunhyeok Lee, Bomi Kim, Dae-Shik Kim
• Abstract summary: Supervised learning-based methods yield robust denoising results, yet they are inherently limited by the need for large-scale datasets. In this study we propose a frequency-sensitive unsupervised denoising method. Results using natural and synthetic datasets indicate that our unsupervised learning method augmented with frequency information achieves state-of-the-art denoising performance.
• Score: 2.834895018689047
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Supervised learning-based methods yield robust denoising results, yet they are inherently limited by the need for large-scale clean/noisy paired datasets. The use of unsupervised denoisers, on the other hand, necessitates a more detailed understanding of the underlying image statistics. In particular, it is well known that apparent differences between clean and noisy images are most prominent on high-frequency bands, justifying the use of low-pass filters as part of conventional image preprocessing steps. However, most learning-based denoising methods utilize only one-sided information from the spatial domain without considering frequency domain information. To address this limitation, in this study we propose a frequency-sensitive unsupervised denoising method. To this end, a generative adversarial network (GAN) is used as a base structure. Subsequently, we include spectral discriminator and frequency reconstruction loss to transfer frequency knowledge into the generator. Results using natural and synthetic datasets indicate that our unsupervised learning method augmented with frequency information achieves state-of-the-art denoising performance, suggesting that frequency domain information could be a viable factor in improving the overall performance of unsupervised learning-based methods.

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