Image denoising is a typical ill-posed problem due to complex degradation. Leading methods based on normalizing flows have tried to solve this problem with an invertible transformation instead of a deterministic mapping. However, the implicit bijective mapping is not explored well. Inspired by a latent observation that noise tends to appear in the high-frequency part of the image, we propose a fully invertible denoising method that injects the idea of disentangled learning into a general invertible neural network to split noise from the high-frequency part. More specifically, we decompose the noisy image into clean low-frequency and hybrid high-frequency parts with an invertible transformation and then disentangle case-specific noise and high-frequency components in the latent space. In this way, denoising is made tractable by inversely merging noiseless low and high-frequency parts. Furthermore, we construct a flexible hierarchical disentangling framework, which aims to decompose most of the low-frequency image information while disentangling noise from the high-frequency part in a coarse-to-fine manner. Extensive experiments on real image denoising, JPEG compressed artifact removal, and medical low-dose CT image restoration have demonstrated that the proposed method achieves competing performance on both quantitative metrics and visual quality, with significantly less computational cost.

翻译：图像去噪因复杂的退化过程而成为一个典型的病态问题。基于归一化流的先进方法试图通过可逆变换而非确定性映射来解决该问题，然而隐式的双射映射尚未得到充分探索。受噪声倾向于出现在图像高频部分这一潜在观察启发，我们提出了一种完全可逆的去噪方法，将解耦学习思想注入通用可逆神经网络，从高频部分分离噪声。具体而言，我们通过可逆变换将含噪图像分解为干净的低频部分与混合高频部分，并在隐空间中解耦特定场景噪声与高频成分。通过这种设计，逆合并无噪的低频与高频部分使得去噪变得可行。此外，我们构建了一个灵活的分层解耦框架，旨在以由粗到精的方式分解大部分低频图像信息，同时从高频部分解耦噪声。在真实图像去噪、JPEG压缩伪影去除及医学低剂量CT图像恢复上的大量实验表明，所提方法在定量指标与视觉质量上均取得了竞争性表现，且计算成本显著降低。