WINNet：基于小波变换的可逆网络图像去噪
WINNet: Wavelet-inspired Invertible Network for Image Denoising
摘要：
图像去噪的目的是从观测到的噪声图像中恢复出干净的图像。基于模型的图像去噪方法在不同的噪声水平下都具有很好的泛化能力，并且具有很高的可解释性。基于学习的方法能够取得较好的效果，但通常具有较弱的泛化能力和可解释性。在本文中，我们提出了一个小波启发的可逆网络（WINNet）结合的优点，基于小波的方法和learningbased方法。建议的WINNet由K尺度的提升启发可逆神经网络（LINNs）和稀疏驱动的去噪网络以及噪声估计网络组成。LINNs的网络结构受到小波提升方案的启发。LINN用于学习具有完美重构特性的非线性冗余变换，以促进噪声去除。去噪网络实现用于去噪的稀疏编码过程。噪声估计网络从输入图像估计噪声水平，其将用于自适应地调整LINN中的软阈值。LINN的正向变换产生用于去噪的冗余多尺度表示。使用具有去噪细节通道和原始粗通道的LINNs的逆变换来重建去噪图像。仿真结果表明，所提出的WINNet方法是高度可解释的，并具有较强的泛化能力，以未知的噪声水平。在非盲/盲图像去噪和图像去模糊方面也取得了较好的效果。
英文摘要：
Image denoising aims to restore a clean image from an observed noisy image. The model-based image denoising approaches can achieve good generalization ability over different noise levels and are with high interpretability. Learning-based approaches are able to achieve better results, but usually with weaker generalization ability and interpretability. In this paper, we propose a wavelet-inspired invertible network (WINNet) to combine the merits of the wavelet-based approaches and learningbased approaches. The proposed WINNet consists of K-scale of lifting inspired invertible neural networks (LINNs) and sparsity-driven denoising networks together with a noise estimation network. The network architecture of LINNs is inspired by the lifting scheme in wavelets. LINNs are used to learn a non-linear redundant transform with perfect reconstruction property to facilitate noise removal. The denoising network implements a sparse coding process for denoising. The noise estimation network estimates the noise level from the input image which will be used to adaptively adjust the soft-thresholds in LINNs. The forward transform of LINNs produce a redundant multi-scale representation for denoising. The denoised image is reconstructed using the inverse transform of LINNs with the denoised detail channels and the original coarse channel. The simulation results show that the proposed WINNet method is highly interpretable and has strong generalization ability to unseen noise levels. It also achieves competitive results in the non-blind/blind image denoising and in image deblurring.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
