Test-time Adaptation for Real Image Denoising via Meta-transfer Learning

Agus Gunawan; Muhammad Adi Nugroho; Se Jin Park

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arXiv · 2207.02066

Test-time Adaptation for Real Image Denoising via Meta-transfer Learning

Agus Gunawan·Muhammad Adi Nugroho·Se Jin Park

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Abstract

In recent years, a ton of research has been conducted on real image denoising tasks. However, the efforts are more focused on improving real image denoising through creating a better network architecture. We explore a different direction where we propose to improve real image denoising performance through a better learning strategy that can enable test-time adaptation on the multi-task network. The learning strategy is two stages where the first stage pre-train the network using meta-auxiliary learning to get better meta-initialization. Meanwhile, we use meta-learning for fine-tuning (meta-transfer learning) the network as the second stage of our training to enable test-time adaptation on real noisy images. To exploit a better learning strategy, we also propose a network architecture with self-supervised masked reconstruction loss. Experiments on a real noisy dataset show the contribution of the proposed method and show that the proposed method can outperform other SOTA methods.

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BibTeX RIS

Agus Gunawan, Muhammad Adi Nugroho, Se Jin Park. 2022-07-05. Test-time Adaptation for Real Image Denoising via Meta-transfer Learning. https://arxiv.org/abs/2207.02066

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