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This paper introduces MDIRNET, a unified deep unfolding network for image restoration that jointly handles noise, rain, and blur degradations without task-specific modules. By integrating a model-driven low-rank prior formalized via Orthogonal Variational PCA with learned components for patch partitioning and dynamic rank allocation, MDIRNET effectively addresses complex, mixed degradations. Experimental results demonstrate competitive or superior performance against strong baselines on single and mixed degradation benchmarks.
A single network now restores images corrupted by noise, rain, and blur simultaneously, outperforming specialized models by leveraging a learned low-rank prior and dynamic subspace allocation.
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.