Super-Resolution with Structured Motion

TL;DR

This paper demonstrates that high-precision motion information, sparse priors, and convex optimization can significantly enhance super-resolution, even turning motion blur into an advantage for reconstructing high-resolution images from low-resolution data.

Contribution

It introduces a novel approach combining motion data, sparse priors, and convex optimization to achieve large resolution increases in super-resolution, challenging traditional limitations.

Findings

Convex optimization enables perfect reconstruction of sparse signals.

Pseudo-random motion can facilitate super-resolution from a single low-res image.

Motion blur can be exploited to improve super-resolution results.

Abstract

We consider the limits of super-resolution using imaging constraints. Due to various theoretical and practical limitations, reconstruction-based methods have been largely restricted to small increases in resolution. In addition, motion-blur is usually seen as a nuisance that impedes super-resolution. We show that by using high-precision motion information, sparse image priors, and convex optimization, it is possible to increase resolution by large factors. A key operation in super-resolution is deconvolution with a box. In general, convolution with a box is not invertible. However, we obtain perfect reconstructions of sparse signals using convex optimization. We also show that motion blur can be helpful for super-resolution. We demonstrate that using pseudo-random motion it is possible to reconstruct a high-resolution target using a single low-resolution image. We present numerical experiments with simulated data and results with real data captured by a camera mounted on a computer controlled stage.