用等变去噪器提升图像修复的先验建模能力
Equivariant Denoisers for Image Restoration
- 设计等变去噪器，让网络自动适应旋转翻转等变换
- 在真实图像修复任务中，显著提升重建质量与稳定性
- 适合需要高鲁棒性图像恢复的研究者和工程师
图像修复的核心在于对干净图像建立合理先验以补全观测缺失信息。当前主流方法依赖神经网络编码该先验。然而，典型图像分布对旋转、翻转等变换具有不变性，而多数深度架构未能体现这一特性。近期工作通过引入等变性，在即插即用框架中缓解此问题。本文提出统一框架ERED（等变去噪正则化），基于等变去噪器与随机优化。我们分析了算法收敛性，并讨论其实际优势。
原文摘要 · Abstract (English)
One key ingredient of image restoration is to define a realistic prior on clean images to complete the missing information in the observation. State-of-the-art restoration methods rely on a neural network to encode this prior. Moreover, typical image distributions are invariant to some set of transformations, such as rotations or flips. However, most deep architectures are not designed to represent an invariant image distribution. Recent works have proposed to overcome this difficulty by including equivariance properties within a Plug-and-Play paradigm. In this work, we propose a unified framework named Equivariant Regularization by Denoising (ERED) based on equivariant denoisers and stochastic optimization. We analyze the convergence of this algorithm and discuss its practical benefit.
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