Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models

2026-05-04 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision, Emerging Technologies & Innovation · Depth: Expert, quick

Summary

A new modular framework for hybrid image restoration integrates transformer and state-space model (SSM) blocks to enhance runtime efficiency on edge hardware. Transformers excel at global modeling but suffer high latency on mobile devices, particularly with high-resolution images. SSMs like Mamba offer linear-time sequence modeling with lower overhead but can underperform on fine-grained restoration. The framework addresses this by training lightweight SSM blocks to act as feature-distilled surrogates for transformer blocks, forming hybrid U-Net-style architectures. To optimize block combinations, the authors introduce Efficient Network Search (ENS), a multi-objective strategy that selects task-specific hybrid configurations from pre-aligned components. ENS optimizes restoration quality while penalizing transformer usage, acting as a proxy for latency. On a Snapdragon 8 Elite CPU, ENS-discovered hybrids significantly reduce inference times compared to the Restormer baseline (10119.52 ms), with ENS-Deblurring running in 2973 ms (3.4x faster), ENS-Deraining in 5816 ms (1.74x faster), and ENS-Denoising in 8666 ms (1.17x faster), all while maintaining competitive restoration quality.

Key takeaway

For research scientists developing image restoration models for edge devices, you should explore hybrid architectures combining transformers and state-space models. This approach, particularly when guided by Efficient Network Search (ENS), can significantly reduce inference latency on mobile CPUs like the Snapdragon 8 Elite while preserving competitive restoration quality. Prioritize methods that automatically balance performance and efficiency to accelerate deployment.

Key insights

Hybridizing transformers with SSMs via distillation improves image restoration efficiency on edge devices.

Principles

• Balance accuracy and efficiency in model design.
• SSMs can serve as efficient transformer surrogates.
• Penalize high-latency components during search.

Method

Train lightweight SSM blocks as feature-distilled surrogates of transformer blocks. Use Efficient Network Search (ENS) to discover optimal hybrid U-Net architectures by optimizing restoration quality and penalizing transformer usage.

In practice

• Use ENS for edge-efficient model discovery.
• Consider SSMs for mobile device deployment.
• Distill transformers into lighter models.

Topics

• Edge-Efficient Image Restoration
• Transformer Distillation
• State-Space Models
• Efficient Network Search
• Hybrid U-Net Architectures

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.