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

Image restoration under diverse degradations remains chal-lenging for unified all-in-one frameworks due to feature interference andinsufficient expert specialization. We propose SLER-IR, a sphericallayer-wise expert routing framework that dynamically activates special-ized experts across network layers. To ensure reliable routing, we in-troduce a Spherical Uniform Degradation Embedding with contrastivelearning, which maps degradation representations onto a hypersphereto eliminate geometry bias in linear embedding spaces. In addition, aGlobal–Local Granularity Fusion (GLGF) module integrates global se-mantics and local degradation cues to address spatially non-uniformdegradations and the train–test granularity gap. Experiments on three-task and five-task benchmarks demonstrate that SLER-IR achievesconsistent improvements over state-of-the-art methods in both PSNRand SSIM. Code and models will be available at https://github.com/PSR666/SLER-IR.