Efficient Difficulty-Aware Dynamic Routing for Diffusion-Based Real-World Image Super-Resolution

TL;DR - A diffusion-based real-world image super-resolution method that routes each input to a network of appropriate capacity based on its estimated restoration difficulty, improving efficiency without sacrificing quality on hard cases.

• Proposes Difficulty-aware Dynamic Routing (DDR): a difficulty estimator predicts each image's restoration cost and assigns it to a suitably-sized Real-ISR network, replacing one-size-fits-all processing.
• Builds multiple Real-ISR networks of varying capacity by modulating the VAE's spatial downsampling ratio in the Stable Diffusion backbone, preserving more high-frequency detail for challenging inputs.
• Targets a known limitation where aggressive VAE downsampling (e.g., 8x) causes irreversible loss of fine details that diffusion cannot recover.
• Authors report superior efficiency and effectiveness versus recent state-of-the-art methods (specific metrics not provided in the abstract).