Image Super-Resolution
Overview
Image super-resolution estimates sharper, higher-resolution images from one or more low-resolution observations, supporting photography, microscopy, remote sensing, and medical imaging when direct high-resolution capture is limited. Methods range from CNNs and attention or state-space Transformers to continuous implicit representations and multi-frame fusion; diffusion, flow-matching, and autoregressive models generate plausible fine detail, with distillation and quantization helping reduce inference costs. Evaluation spans synthetic benchmarks using PSNR and SSIM, real-world perceptual measures and human judgments, and task-specific tests such as recognition or segmentation. A central tension is that realistic textures can be hallucinated or depart from observed content, while synthetic training and pixel metrics may not predict performance on real sensors.
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