Learning What to Degrade: A Recoverability-Aware Curriculum for Reliable Smart-Glasses Super-Resolution

Images captured by smart glasses often suffer from complex degradations, obscuring small text and fine-grained structures essential for visual assistance. Although super-resolution (SR) can improve perceptual quality, these gains do not necessarily translate into reliable downstream understanding by vision-language models (VLMs). We introduce a high-resolution (HR)-referenced semantic evaluation protocol that compares VLM responses to restored images with those obtained from corresponding HR references. The evaluation reveals two failure modes: generative SR can introduce misleading details, while restoration-oriented SR can leave critical details blurred or ambiguous. To improve detail recovery in restoration-oriented SR, we investigate adaptive degradation scheduling. High reconstruction error may reflect either insufficient model capability or excessive information loss, requiring different adjustments to degradation severity. We therefore propose ReDiC, a Recoverability–Difficulty Curriculum that jointly estimates input recoverability and model-relative restoration difficulty to adaptively control degradation severity during training. The scheduler increases severity under easy and recoverable conditions, maintains challenging but recoverable conditions, and reduces severity when estimated recoverability is low. ReDiC modifies only degradation generation during training and is compatible with existing SR architectures. Experiments on synthetic degradation benchmarks show consistent improvements across NAFNet, SwinIR, and MambaIR, yielding average PSNR gains of 2.36–2.65 dB over their corresponding baselines while improving downstream recognition and HR-referenced semantic agreement. Both automatic and human evaluations on real smart-glasses captures show improved downstream semantic agreement across all three backbones.