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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Neural networks can be parametrized to compute exact proximal operators that are provably equivariant to affine transformations, yielding better out-of-distribution performance on inverse problems.
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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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Learning Affine-Equivariant Proximal Operators
Neural networks can be parametrized to compute exact proximal operators that are provably equivariant to affine transformations, yielding better out-of-distribution performance on inverse problems.