Restoring images affected by various types of degradation, such as noise, blur, or improper exposure, remains a significant challenge in computer vision. While recent trends favor complex monolithic all-in-one architectures, these models often suffer from negative task interference and require extensive joint training cycles on high-end computing clusters. In this paper, we propose a modular, task-decoupled image restoration framework based on an explicit diagnostic routing mechanism. The architecture consists of a lightweight Convolutional Neural Network (CNN) classifier that evaluates the input image and dynamically directs it to a specialized restoration node. A key advantage of this framework is its model-agnostic extensibility: while we demonstrate it using three independent U-Net experts, the system allows for the integration of any restoration method tailored to specific tasks. By isolating reconstruction paths, the framework prevents feature conflicts and significantly reduces training overhead. Unlike monolithic models, adding new degradation types in our framework only requires training a single expert and updating the router, rather than a full system retraining. Experimental results demonstrate that this computationally accessible approach offers a scalable and efficient solution for multi-degradation restoration on standard local hardware. The code will be published upon paper acceptance.

翻译：修复受噪声、模糊或曝光不当等多种退化类型影响的图像，仍是计算机视觉领域的一项重大挑战。尽管近期趋势倾向于复杂的单一整体式架构，但这些模型常受负面的任务干扰，且需在高端计算集群上进行大量联合训练。本文提出一种基于显式诊断路由机制的模块化、任务解耦的图像修复框架。该架构由一个轻量级卷积神经网络（CNN）分类器组成，负责评估输入图像并动态将其引导至专门的修复节点。该框架的一个关键优势在于其模型无关的可扩展性：虽然我们使用三个独立的U-Net专家进行演示，但该系统允许集成任何针对特定任务定制的修复方法。通过隔离重建路径，该框架可防止特征冲突并显著降低训练开销。与整体式模型不同，在框架中添加新的退化类型仅需训练单个专家并更新路由器，而非重新训练整个系统。实验结果表明，这种计算上可行的途径为在标准本地硬件上实现多退化修复提供了一种可扩展且高效的解决方案。代码将在论文接收后公开。