46 research outputs found
Motion Deblurring in the Wild
The task of image deblurring is a very ill-posed problem as both the image
and the blur are unknown. Moreover, when pictures are taken in the wild, this
task becomes even more challenging due to the blur varying spatially and the
occlusions between the object. Due to the complexity of the general image model
we propose a novel convolutional network architecture which directly generates
the sharp image.This network is built in three stages, and exploits the
benefits of pyramid schemes often used in blind deconvolution. One of the main
difficulties in training such a network is to design a suitable dataset. While
useful data can be obtained by synthetically blurring a collection of images,
more realistic data must be collected in the wild. To obtain such data we use a
high frame rate video camera and keep one frame as the sharp image and frame
average as the corresponding blurred image. We show that this realistic dataset
is key in achieving state-of-the-art performance and dealing with occlusions
DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networks
We present DeblurGAN, an end-to-end learned method for motion deblurring. The
learning is based on a conditional GAN and the content loss . DeblurGAN
achieves state-of-the art performance both in the structural similarity measure
and visual appearance. The quality of the deblurring model is also evaluated in
a novel way on a real-world problem -- object detection on (de-)blurred images.
The method is 5 times faster than the closest competitor -- DeepDeblur. We also
introduce a novel method for generating synthetic motion blurred images from
sharp ones, allowing realistic dataset augmentation.
The model, code and the dataset are available at
https://github.com/KupynOrest/DeblurGANComment: CVPR 2018 camera-read
Generalized Video Deblurring for Dynamic Scenes
Several state-of-the-art video deblurring methods are based on a strong
assumption that the captured scenes are static. These methods fail to deblur
blurry videos in dynamic scenes. We propose a video deblurring method to deal
with general blurs inherent in dynamic scenes, contrary to other methods. To
handle locally varying and general blurs caused by various sources, such as
camera shake, moving objects, and depth variation in a scene, we approximate
pixel-wise kernel with bidirectional optical flows. Therefore, we propose a
single energy model that simultaneously estimates optical flows and latent
frames to solve our deblurring problem. We also provide a framework and
efficient solvers to optimize the energy model. By minimizing the proposed
energy function, we achieve significant improvements in removing blurs and
estimating accurate optical flows in blurry frames. Extensive experimental
results demonstrate the superiority of the proposed method in real and
challenging videos that state-of-the-art methods fail in either deblurring or
optical flow estimation.Comment: CVPR 2015 ora
Online Video Deblurring via Dynamic Temporal Blending Network
State-of-the-art video deblurring methods are capable of removing non-uniform
blur caused by unwanted camera shake and/or object motion in dynamic scenes.
However, most existing methods are based on batch processing and thus need
access to all recorded frames, rendering them computationally demanding and
time consuming and thus limiting their practical use. In contrast, we propose
an online (sequential) video deblurring method based on a spatio-temporal
recurrent network that allows for real-time performance. In particular, we
introduce a novel architecture which extends the receptive field while keeping
the overall size of the network small to enable fast execution. In doing so,
our network is able to remove even large blur caused by strong camera shake
and/or fast moving objects. Furthermore, we propose a novel network layer that
enforces temporal consistency between consecutive frames by dynamic temporal
blending which compares and adaptively (at test time) shares features obtained
at different time steps. We show the superiority of the proposed method in an
extensive experimental evaluation.Comment: 10 page
Joint Blind Motion Deblurring and Depth Estimation of Light Field
Removing camera motion blur from a single light field is a challenging task
since it is highly ill-posed inverse problem. The problem becomes even worse
when blur kernel varies spatially due to scene depth variation and high-order
camera motion. In this paper, we propose a novel algorithm to estimate all blur
model variables jointly, including latent sub-aperture image, camera motion,
and scene depth from the blurred 4D light field. Exploiting multi-view nature
of a light field relieves the inverse property of the optimization by utilizing
strong depth cues and multi-view blur observation. The proposed joint
estimation achieves high quality light field deblurring and depth estimation
simultaneously under arbitrary 6-DOF camera motion and unconstrained scene
depth. Intensive experiment on real and synthetic blurred light field confirms
that the proposed algorithm outperforms the state-of-the-art light field
deblurring and depth estimation methods
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