22,408 research outputs found
Structured Kernel Estimation for Photon-Limited Deconvolution
Images taken in a low light condition with the presence of camera shake
suffer from motion blur and photon shot noise. While state-of-the-art image
restoration networks show promising results, they are largely limited to
well-illuminated scenes and their performance drops significantly when photon
shot noise is strong.
In this paper, we propose a new blur estimation technique customized for
photon-limited conditions. The proposed method employs a gradient-based
backpropagation method to estimate the blur kernel. By modeling the blur kernel
using a low-dimensional representation with the key points on the motion
trajectory, we significantly reduce the search space and improve the regularity
of the kernel estimation problem. When plugged into an iterative framework, our
novel low-dimensional representation provides improved kernel estimates and
hence significantly better deconvolution performance when compared to
end-to-end trained neural networks. The source code and pretrained models are
available at \url{https://github.com/sanghviyashiitb/structured-kernel-cvpr23}Comment: main document and supplementary; accepted at CVPR202
Mitigating Motion Blur for Robust 3D Baseball Player Pose Modeling for Pitch Analysis
Using videos to analyze pitchers in baseball can play a vital role in
strategizing and injury prevention. Computer vision-based pose analysis offers
a time-efficient and cost-effective approach. However, the use of accessible
broadcast videos, with a 30fps framerate, often results in partial body motion
blur during fast actions, limiting the performance of existing pose keypoint
estimation models. Previous works have primarily relied on fixed backgrounds,
assuming minimal motion differences between frames, or utilized multiview data
to address this problem. To this end, we propose a synthetic data augmentation
pipeline to enhance the model's capability to deal with the pitcher's blurry
actions. In addition, we leverage in-the-wild videos to make our model robust
under different real-world conditions and camera positions. By carefully
optimizing the augmentation parameters, we observed a notable reduction in the
loss by 54.2% and 36.2% on the test dataset for 2D and 3D pose estimation
respectively. By applying our approach to existing state-of-the-art pose
estimators, we demonstrate an average improvement of 29.2%. The findings
highlight the effectiveness of our method in mitigating the challenges posed by
motion blur, thereby enhancing the overall quality of pose estimation.Comment: Accepted in the 6th International Workshop on Multimedia Content
Analysis in Sports (MMSports'23) @ ACM Multimedi
Simultaneous Stereo Video Deblurring and Scene Flow Estimation
Videos for outdoor scene often show unpleasant blur effects due to the large
relative motion between the camera and the dynamic objects and large depth
variations. Existing works typically focus monocular video deblurring. In this
paper, we propose a novel approach to deblurring from stereo videos. In
particular, we exploit the piece-wise planar assumption about the scene and
leverage the scene flow information to deblur the image. Unlike the existing
approach [31] which used a pre-computed scene flow, we propose a single
framework to jointly estimate the scene flow and deblur the image, where the
motion cues from scene flow estimation and blur information could reinforce
each other, and produce superior results than the conventional scene flow
estimation or stereo deblurring methods. We evaluate our method extensively on
two available datasets and achieve significant improvement in flow estimation
and removing the blur effect over the state-of-the-art methods.Comment: Accepted to IEEE International Conference on Computer Vision and
Pattern Recognition (CVPR) 201
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
In this paper, we address the problem of estimating and removing non-uniform
motion blur from a single blurry image. We propose a deep learning approach to
predicting the probabilistic distribution of motion blur at the patch level
using a convolutional neural network (CNN). We further extend the candidate set
of motion kernels predicted by the CNN using carefully designed image
rotations. A Markov random field model is then used to infer a dense
non-uniform motion blur field enforcing motion smoothness. Finally, motion blur
is removed by a non-uniform deblurring model using patch-level image prior.
Experimental evaluations show that our approach can effectively estimate and
remove complex non-uniform motion blur that is not handled well by previous
approaches.Comment: This is a final version accepted by CVPR 201
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