Abstract
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.
Abstract (translated)
图像去噪是一个由于复杂退化而典型的不可约问题。基于正常化流的主要方法试图使用可逆变换而不是确定性映射来解决这个问题。然而,隐含的双向映射并没有得到充分的探索。受到一个潜在的观察,即图像中的噪声往往出现在高频部分,因此我们提出了一种全可逆去噪方法,将分离学习的理念注入到一般可逆神经网络中,从高频部分分离噪声。更具体地说,我们使用可逆变换将噪声图像分解成干净的频率和混合高频部分,然后从潜在空间中分离特定情况下的噪声和高频成分。通过反向合并无噪声的低和高频率部分,去噪变得易于处理。此外,我们建立了一个灵活的Hierarchical denoising框架,旨在分解大部分低频率图像信息,同时从高频部分分离噪声。在实际应用中,针对真实图像去噪、JPEG压缩的 artifacts移除和医学低剂量CT图像恢复进行了广泛的实验,证明了该方法在 quantitative metrics 和视觉质量方面实现了竞争性能,计算成本显著更低。
URL
https://arxiv.org/abs/2301.13358
