SLER-IR：面向一体化图像复原的球面分层专家路由机制

摘要

针对多样化退化条件下的图像复原任务，统一的全能框架常因特征干扰与专家专业性不足而面临挑战。我们提出SLER-IR框架——一种球面分层专家路由机制，通过动态激活网络各层的专用专家模块实现精准复原。为确保路由可靠性，我们引入基于对比学习的球面均匀退化嵌入技术，将退化表征映射至超球面空间，从而消除线性嵌入空间的几何偏差。此外，全局-局部粒度融合（GLGF）模块通过整合全局语义与局部退化线索，有效解决空间非均匀退化问题与训练-测试粒度差异。在三任务与五任务基准测试上的实验表明，SLER-IR在PSNR与SSIM指标上均较现有最优方法取得持续提升。代码与模型将公开发布。

English

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.

SLER-IR：面向一体化图像复原的球面分层专家路由机制

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

摘要

Support