1,940 research outputs found
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
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
The World of Fast Moving Objects
The notion of a Fast Moving Object (FMO), i.e. an object that moves over a
distance exceeding its size within the exposure time, is introduced. FMOs may,
and typically do, rotate with high angular speed. FMOs are very common in
sports videos, but are not rare elsewhere. In a single frame, such objects are
often barely visible and appear as semi-transparent streaks.
A method for the detection and tracking of FMOs is proposed. The method
consists of three distinct algorithms, which form an efficient localization
pipeline that operates successfully in a broad range of conditions. We show
that it is possible to recover the appearance of the object and its axis of
rotation, despite its blurred appearance. The proposed method is evaluated on a
new annotated dataset. The results show that existing trackers are inadequate
for the problem of FMO localization and a new approach is required. Two
applications of localization, temporal super-resolution and highlighting, are
presented
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
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