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.

本研究提出了一种利用图像深度信息，共同估计 6 自由度运动（6 DoF）相机运动和去除由于相机运动引起的不均匀模糊的方法，并将其制定为能量最小化问题，该方法能有效地解决相机抖动时的图像模糊问题。

带深度图的单张图像去模糊和相机运动估计

Single Image Deblurring and Camera Motion Estimation with Depth Map

Removing blur caused by camera shake in images has always been a challenging
problem in computer vision literature due to its ill-posed nature. Motion blur
caused due to the relative motion between the camera and the object in 3D space
induces a spatially varying blurring effect over the entire image. In this
paper, we propose a novel deep filter based on Generative Adversarial Network
(GAN) architecture integrated with global skip connection and dense
architecture in order to tackle this problem. Our model, while bypassing the
process of blur kernel estimation, significantly reduces the test time which is
necessary for practical applications. The experiments on the benchmark datasets
prove the effectiveness of the proposed method which outperforms the
state-of-the-art blind deblurring algorithms both quantitatively and
qualitatively.

本文提出了一种新颖的基于生成对抗网络（GAN）的深度滤波器，其与全局跳跃连接和密集结构相集成，以解决由于相机和物体之间的相对运动引起的运动模糊问题。在不需要进行模糊核估计的情况下，我们的模型显著减少了测试时间，证明了该方法在定量和定性上均胜过现有的盲去模糊算法，具有实际应用前景。

深度生成滤波器用于运动去模糊

Deep Generative Filter for Motion Deblurring

Motion blur from camera shake is a major problem in videos captured by
hand-held devices. Unlike single-image deblurring, video-based approaches can
take advantage of the abundant information that exists across neighboring
frames. As a result the best performing methods rely on aligning nearby frames.
However, aligning images is a computationally expensive and fragile procedure,
and methods that aggregate information must therefore be able to identify which
regions have been accurately aligned and which have not, a task which requires
high level scene understanding. In this work, we introduce a deep learning
solution to video deblurring, where a CNN is trained end-to-end to learn how to
accumulate information across frames. To train this network, we collected a
dataset of real videos recorded with a high framerate camera, which we use to
generate synthetic motion blur for supervision. We show that the features
learned from this dataset extend to deblurring motion blur that arises due to
camera shake in a wide range of videos, and compare the quality of results to a
number of other baselines.

本文提出一种基于深度学习的视频去模糊算法，利用卷积神经网络从相邻帧中积累信息，可处理各类由于相机抖动导致的运动模糊问题。