Degradation-Aware Image Restoration

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32 papers8 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration
Mar 6, 2026Peng Shurui, Xin Lin, Shi Luo +5Image Restoration All-in-One Image Restoration
Sichuan University · University of California San Diego · Insta360 Research
RestoRect: Degraded Image Restoration via Latent Rectified Flow & Feature Distillation
Sep 27, 2025Shourya Verma, Mengbo Wang, Nadia Atallah Lanman +1Image Restoration Feature-Based Knowledge Distillation
Department of Computer Science, Purdue University, USA · Purdue Institute of Cancer Research, USA

Mar 6, 2026J/K move · Enter open · S save

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

Peng Shurui, Xin Lin, Shi Luo, Jincen Ou+4

Sichuan University · University of California San Diego · Insta360 Research

Image Restoration All-in-One Image Restoration Degradation-Aware Image Restoration Mixture of Experts

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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.

Latest papers

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

RestoRect: Degraded Image Restoration via Latent Rectified Flow & Feature Distillation