发表机构
Lahore University of Management Sciences; COMSATS University（拉合尔管理科学大学; 科慕萨茨大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
MDIRNET通过深度展开将低秩先验与端到端学习统一，联合处理噪声、雨和模糊退化，无需任务特定模型，在多个基准上达到或优于强基线。
AI 中文摘要
真实图像通常表现出未知且混合的退化，使得恢复比单任务图像恢复更具挑战性，因为多种失真类型在同一观测中相互作用。因此，现有方法往往依赖于退化类型的先验知识或单独的任务特定模型，这可能会过度平滑精细结构或留下残余伪影，从而促使一种紧凑的模型驱动替代方案。我们提出了多退化图像恢复网络（MDIRNET），这是一个统一框架，结合了模型驱动的低秩先验与端到端学习。这里的“统一”指的是在三种退化类型（噪声、雨、模糊）上进行联合训练。单个MDIRNET模型恢复所有三种退化，无需在推理时使用任务特定的模型、模块或分支。低秩先验利用了自然图像块的红余和紧凑结构。为了识别这种潜在的低维表示，我们通过正交变分PCA（OVPCA）形式化恢复，并将其迭代推理转化为深度展开网络。为了处理空间不均匀的损坏和局部内容变异性，我们进一步引入了可学习的块划分策略和轻量级动态秩分配模块，该模块预测每个区域的适当子空间维度。空间自适应重建细化通过监督注意力模块执行。在标准去噪、去模糊和去雨基准上的大量实验表明，MDIRNET在大多数指标上达到或优于强基线，而受控混合退化实验在评估的合成退化组合中表现一致。代码可在以下网址获取：此https URL。
英文摘要
Real images often exhibit unknown and mixed degradations, making restoration substantially more challenging than single-task image restoration because multiple distortion types interact within the same observation. Consequently, existing methods often rely on prior knowledge of the degradation type or separate task-specific models, which may oversmooth fine structures or leave residual artifacts, motivating a compact model-driven alternative. We propose the Multi-Degradation Image Restoration Network (MDIRNET), a unified framework that combines a model-driven low-rank prior with end-to-end learning. Here, unified refers to joint training on three degradation types: noise, rain, and blur. A single MDIRNET model restores all three without requiring task-specific models, modules, or branches at inference. The low-rank prior exploits the redundancy and compact structure of natural image patches. To identify this underlying low-dimensional representation, we formalize restoration via Orthogonal Variational PCA (OVPCA) and translate its iterative inference into a deep unfolding network. To handle spatially non-uniform corruption and local content variability, we further introduce a learnable patch-partitioning strategy and a lightweight dynamic rank-allocation module that predicts the appropriate subspace dimension for each region. Spatially adaptive reconstruction refinement is performed using a supervised attention module. Extensive experiments on standard denoising, deblurring, and deraining benchmarks show that MDIRNET achieves competitive or superior performance over strong baselines across most metrics, while controlled mixed-degradation experiments demonstrate consistent performance across the evaluated synthetic degradation combinations. The code is available at https://github.com/ScholarForge/mdirnet.git.
CommentsAccepted for publication in IEEE Transactions on Instrumentation and Measurement (IEEE TIM), 2026
