  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.
