DeepAI AI Chat
Log In Sign Up

Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution from a Blurred Image Sequence

by   Haesol Park, et al.
Seoul National University

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.


page 1

page 2

page 7

page 8


Deep Residual Network for Joint Demosaicing and Super-Resolution

In digital photography, two image restoration tasks have been studied ex...

Improving Multi-View Stereo via Super-Resolution

Today, Multi-View Stereo techniques are able to reconstruct robust and d...

Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach

A novel approach towards depth map super-resolution using multi-view unc...

Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss

We present a novel unsupervised learning framework for single view depth...

Depth Reconstruction from Sparse Samples: Representation, Algorithm, and Sampling

The rapid development of 3D technology and computer vision applications ...

POSEAMM: A Unified Framework for Solving Pose Problems using an Alternating Minimization Method

Pose estimation is one of the most important problems in computer vision...

Guided Super-Resolution as a Learned Pixel-to-Pixel Transformation

Guided super-resolution is a unifying framework for several computer vis...