提出可解释的多退化图像修复框架，兼顾性能与物理意义。
Physically Interpretable Multi-Degradation Image Restoration via Deep Unfolding and Explainable Convolution
- 用深度展开技术将优化算法转为可学习网络，每层有明确物理含义。
- 在多个退化组合上达到领先效果，单退化任务也表现优异。
- 适合需要模型透明性与灵活适应性的实际图像修复场景。
尽管图像修复技术已取得显著进展，但多数方法仅针对单一退化类型。真实场景中，图像常同时存在雨痕、噪声、雾霾等多种退化，要求模型具备处理多种退化的能力。此外，通过堆叠模块提升性能的方法往往缺乏可解释性。本文提出一种基于可解释性驱动的多退化图像修复新方法，构建于深度展开网络之上，将数学优化算法的迭代过程映射为可学习的网络结构。具体而言，采用改进的二阶半光滑牛顿算法，确保每个模块保持清晰的物理可解释性。为进一步提升可解释性与适应性，设计了一种受人脑信息处理机制和图像内在特性启发的可解释卷积模块，使网络能灵活调用知识并自主调整参数以适应不同输入。所提出的紧密集成架构InterIR在多退化修复任务中表现出色，同时在单退化任务上也保持高度竞争力。
原文摘要 · Abstract (English)
Although image restoration has advanced significantly, most existing methods target only a single type of degradation. In real-world scenarios, images often contain multiple degradations simultaneously, such as rain, noise, and haze, requiring models capable of handling diverse degradation types. Moreover, methods that improve performance through module stacking often suffer from limited interpretability. In this paper, we propose a novel interpretability-driven approach for multi-degradation image restoration, built upon a deep unfolding network that maps the iterative process of a mathematical optimization algorithm into a learnable network structure. Specifically, we employ an improved second-order semi-smooth Newton algorithm to ensure that each module maintains clear physical interpretability. To further enhance interpretability and adaptability, we design an explainable convolution module inspired by the human brain's flexible information processing and the intrinsic characteristics of images, allowing the network to flexibly leverage learned knowledge and autonomously adjust parameters for different input. The resulting tightly integrated architecture, named InterIR, demonstrates excellent performance in multi-degradation restoration while remaining highly competitive on single-degradation tasks.
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