🤖 AI Summary
This work addresses the challenges of task interference and high training costs in monolithic models for multi-degradation image restoration by proposing a modular, task-decoupled unified framework. The approach employs a lightweight CNN-based router to diagnose the degradation type of an input image and dynamically routes it to a dedicated U-Net expert model for on-demand restoration. Its key innovation lies in an explicit diagnosis-and-routing mechanism that enables model-agnostic, flexible scalability: incorporating a new degradation type requires training only a single expert module and fine-tuning the router, without retraining the entire system. Experimental results demonstrate that the proposed framework significantly reduces training overhead on standard hardware, avoids feature interference among tasks, and achieves superior performance compared to complex monolithic architectures.
📝 Abstract
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.