让噪声自适应，提升图像去噪模型对未知噪声的泛化能力。
LAN: Learning to Adapt Noise for Image Denoising
- 在输入端学习可调噪声偏移，不修改网络参数。
- 对未见过的噪声类型，去噪性能显著提升。
- 适合需要快速适配新噪声场景的应用者。
图像去噪任务因噪声类型和强度差异大而极具挑战性，尤其当图像来自训练中未见的相机型号或拍摄环境时。尽管深度学习和真实世界数据集带来了显著进步，现有去噪网络在面对新噪声分布时性能下降。本文提出一种新思路：不调整网络，而是自适应输入噪声本身。通过引入可学习的噪声偏移量，直接对输入图像添加修正，使其更接近网络训练所针对的噪声分布。该方法保持预训练网络冻结，仅通过调整输入噪声实现性能提升，有效缓解了对未知噪声的敏感问题，验证了该方向的潜力。代码已开源。
原文摘要 · Abstract (English)
Removing noise from images, a.k.a image denoising, can be a very challenging task since the type and amount of noise can greatly vary for each image due to many factors including a camera model and capturing environments. While there have been striking improvements in image denoising with the emergence of advanced deep learning architectures and real-world datasets, recent denoising networks struggle to maintain performance on images with noise that has not been seen during training. One typical approach to address the challenge would be to adapt a denoising network to new noise distribution. Instead, in this work, we shift our focus to adapting the input noise itself, rather than adapting a network. Thus, we keep a pretrained network frozen, and adapt an input noise to capture the fine-grained deviations. As such, we propose a new denoising algorithm, dubbed Learning-to-Adapt-Noise (LAN), where a learnable noise offset is directly added to a given noisy image to bring a given input noise closer towards the noise distribution a denoising network is trained to handle. Consequently, the proposed framework exhibits performance improvement on images with unseen noise, displaying the potential of the proposed research direction. The code is available at https://github.com/chjinny/LAN
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