The conventional methods for estimating camera poses and scene structures
from severely blurry or low resolution images often result in failure. The
off-the-shelf deblurring or super-resolution methods may show visually pleasing
results. However, applying each technique independently before matching is
generally unprofitable because this naive series of procedures ignores the
consistency between images. In this paper, we propose a pioneering unified
framework that solves four problems simultaneously, namely, dense depth
reconstruction, camera pose estimation, super-resolution, and deblurring. By
reflecting a physical imaging process, we formulate a cost minimization problem
and solve it using an alternating optimization technique. The experimental
results on both synthetic and real videos show high-quality depth maps derived
from severely degraded images that contrast the failures of naive multi-view
stereo methods. Our proposed method also produces outstanding deblurred and
super-resolved images unlike the independent application or combination of
conventional video deblurring, super-resolution methods.

本研究提出了一种创新性的统一框架来解决密集深度重建、相机姿态估计、超分辨率和去模糊的四个问题，通过反映物理成像过程，我们提出了一个成本最小化问题，并使用交替优化技术来解决它，实验结果表明，我们的提出方法在合成和现实视频中均得到高质量的深度图，同时还产生了优秀的去模糊和超分辨率图像。