An MLLM-guided architecture with a mixture of frequency experts and relational alignment loss achieves state-of-the-art all-in-one image restoration, outperforming prior methods by up to 1.35 dB on the CDD11 dataset.
arXiv preprint arXiv:2312.05038 (2023) MMFE-IR 17
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DRNet uses initialization-stage dynamic reparameterization, a guided DRMLP, and a wavelet encoder to deliver efficient all-in-one image restoration across multiple tasks.
CEA assembles per-token low-rank residual updates via dense affinities over hyper-adapter-generated components to improve all-in-one image restoration on spatially non-uniform degradations.
SEGD decouples infrared degradations via degradation-specific residual modules, an evidential perception network, and structural-entropy path selection to surpass prior all-in-one methods with fewer parameters.
citing papers explorer
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Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts
An MLLM-guided architecture with a mixture of frequency experts and relational alignment loss achieves state-of-the-art all-in-one image restoration, outperforming prior methods by up to 1.35 dB on the CDD11 dataset.
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DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization
DRNet uses initialization-stage dynamic reparameterization, a guided DRMLP, and a wavelet encoder to deliver efficient all-in-one image restoration across multiple tasks.
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Continuous Expert Assembly: Instance-Conditioned Low-Rank Residuals for All-in-One Image Restoration
CEA assembles per-token low-rank residual updates via dense affinities over hyper-adapter-generated components to improve all-in-one image restoration on spatially non-uniform degradations.
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Breaking Degradation Coupling: A Structural Entropy Guided Decoupled Framework and Benchmark for Infrared Enhancement
SEGD decouples infrared degradations via degradation-specific residual modules, an evidential perception network, and structural-entropy path selection to surpass prior all-in-one methods with fewer parameters.