Summary of Lan: Learning to Adapt Noise For Image Denoising, by Changjin Kim et al.
LAN: Learning to Adapt Noise for Image Denoising
by Changjin Kim, Tae Hyun Kim, Sungyong Baik
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new approach to image denoising, dubbed Learning-to-Adapt-Noise (LAN), which adapts the input noise rather than adapting the denoising network. The algorithm adds a learnable noise offset to a given noisy image, bringing it closer to the noise distribution that the denoising network is trained to handle. This approach improves performance on images with unseen noise, demonstrating the potential of this research direction. The proposed framework uses a pretrained denoising network and applies it to real-world datasets, showcasing its effectiveness in removing noise from images. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Image denoising can be a challenging task because noise levels and types vary greatly depending on camera models and environments. While deep learning architectures have improved image denoising, recent networks struggle with unseen noise. This paper introduces LAN, which adapts the input noise instead of the network. The algorithm adds a learnable offset to noisy images, bringing them closer to the training noise distribution. This approach shows promise in improving performance on unseen noise. |
Keywords
» Artificial intelligence » Deep learning » Image denoising
