Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond

• URL: http://arxiv.org/abs/2301.13358v1
• Date: Tue, 31 Jan 2023 01:24:34 GMT
• Title: Hierarchical Disentangled Representation for Invertible Image Denoising and Beyond
• Authors: Wenchao Du, Hu Chen, Yi Zhang, and H. Yang
• Abstract summary: 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. We decompose the noisy image into clean low-frequency and hybrid high-frequency parts with an invertible transformation. In this way, denoising is made tractable by inversely merging noiseless low and high-frequency parts.
• Score: 14.432771193620702
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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