# Single Image Deblurring and Camera Motion Estimation with Depth Map

## TL;DR

This paper introduces a method to jointly estimate 6 DoF camera motion and deblur images caused by camera shake using a single blurry image and its depth map, improving real-world deblurring accuracy.

## Contribution

It presents a novel joint framework for deblurring and camera motion estimation that handles complex 6 DoF motion using geometric relationships and energy minimization.

## Key findings

- Effective in real-world and synthetic datasets
- Accurately estimates 6 DoF camera motion
- Produces sharp images from single blurry inputs

## Abstract

Camera shake during exposure is a major problem in hand-held photography, as it causes image blur that destroys details in the captured images.~In the real world, such blur is mainly caused by both the camera motion and the complex scene structure.~While considerable existing approaches have been proposed based on various assumptions regarding the scene structure or the camera motion, few existing methods could handle the real 6 DoF camera motion.~In this paper, we propose to jointly estimate the 6 DoF camera motion and remove the non-uniform blur caused by camera motion by exploiting their underlying geometric relationships, with a single blurry image and its depth map (either direct depth measurements, or a learned depth map) as input.~We formulate our joint deblurring and 6 DoF camera motion estimation as an energy minimization problem which is solved in an alternative manner. Our model enables the recovery of the 6 DoF camera motion and the latent clean image, which could also achieve the goal of generating a sharp sequence from a single blurry image. Experiments on challenging real-world and synthetic datasets demonstrate that image blur from camera shake can be well addressed within our proposed framework.