SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration

• URL: http://arxiv.org/abs/2603.05940v1
• Date: Fri, 06 Mar 2026 06:16:57 GMT
• Title: SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration
• Abstract summary: SLER-IR is a spherical layer-wise expert routing framework that activates specialized experts across network layers.<n>To ensure reliable routing, we introduce a Spherical Uniform Degradation Embedding with contrastive learning.<n>A Global-Local Granularity Fusion module integrates global semantics and local degradation cues to address spatially non-uniform degradations.
• Score: 23.475659268865186
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image restoration under diverse degradations remains challenging for unified all-in-one frameworks due to feature interference and insufficient expert specialization. We propose SLER-IR, a spherical layer-wise expert routing framework that dynamically activates specialized experts across network layers. To ensure reliable routing, we introduce a Spherical Uniform Degradation Embedding with contrastive learning, which maps degradation representations onto a hypersphere to eliminate geometry bias in linear embedding spaces. In addition, a Global-Local Granularity Fusion (GLGF) module integrates global semantics and local degradation cues to address spatially non-uniform degradations and the train-test granularity gap. Experiments on three-task and five-task benchmarks demonstrate that SLER-IR achieves consistent improvements over state-of-the-art methods in both PSNR and SSIM. Code and models will be publicly released.

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