Super-Resolution with Structured Motion (arxiv.org)

Researchers have unveiled a novel approach to super-resolution imaging that leverages structured motion blur as a beneficial factor rather than a hindrance. Traditionally, super-resolution methods have struggled to significantly enhance image resolution due to theoretical and practical constraints, often limiting improvements to small increments. This new technique uses precise motion information, sparse image priors, and convex optimization to achieve large-resolution gains from a single low-resolution image. Contrary to conventional wisdom that views motion blur as noise, the team demonstrates that pseudo-random motion blur can actually facilitate the reconstruction of high-resolution targets. A central challenge in super-resolution is deconvolution with a box function, which is generally non-invertible. However, by employing convex optimization on sparse signals, the researchers achieve perfect reconstructions despite these limitations. Their method was validated through both simulated data and real-world experiments involving a camera mounted on a computer-controlled stage, showing practical feasibility. This work opens exciting new pathways for super-resolution applications, pushing the boundaries of what’s achievable from limited imaging data by turning motion blur from a problem into a useful signal, with significant implications for computer vision and imaging systems in AI and ML.