利用精确运动信息，单张模糊图像也能实现大幅超分辨率。
Super-Resolution with Structured Motion
- 结合高精度运动信息与凸优化，突破传统超分辨限制。
- 仅用一张低分辨率图像，实现显著分辨率提升。
- 运动模糊反而助力重建，适合精密成像场景。
本文研究在成像约束下超分辨率的极限。由于理论与实践限制，基于重建的方法通常只能实现小幅度分辨率提升。运动模糊常被视为干扰因素，但本文证明，借助高精度运动信息、稀疏图像先验和凸优化，可实现大幅分辨率增强。超分辨率中的关键操作是带盒状核的去卷积，通常不可逆。但通过凸优化，可完美重建稀疏信号。进一步表明，伪随机运动有助于超分辨率。实验显示，仅用一张低分辨率图像即可重建高分辨率目标。研究包含模拟数据数值实验及实际相机在计算机控制平台拍摄的数据结果。
原文摘要 · Abstract (English)
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.
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