SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration
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.
69ae6b3b9893f58272f74189
Peng Shurui
69ae6b3b9893f58272f7418a
Xin Lin
69ae6b3b9893f58272f7418b
Shi Luo
69ae6b3b9893f58272f7418c
Jincen Ou
69ae6b3b9893f58272f7418d
Dizhe Zhang
69ae6b3b9893f58272f7418e
Lu Qi
69ae6b3b9893f58272f7418f
Truong Nguyen
69ae6b3b9893f58272f74190
Chao Ren
SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration
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.
A spherical layer-wise expert routing framework with contrastive learning and granularity fusion enables effective image restoration across diverse degradation types.
spherical layer-wise expert routing contrastive learning spherical uniform degradation embedding global-local granularity fusion image restoration degradation representations hypersphere feature interference expert specialization