Super-Resolution with Structured Motion
This work addresses limitations in super-resolution for imaging applications, offering a novel approach that could enhance image quality in fields like photography or medical imaging, though it appears incremental in combining existing techniques like convex optimization with new insights on motion.
The paper tackles the problem of super-resolution by leveraging high-precision motion information and sparse priors to achieve large resolution increases, demonstrating perfect reconstructions of sparse signals and showing that motion blur can be beneficial rather than a nuisance.
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.