2,986 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
A fast and exact -stacking and -projection hybrid algorithm for wide-field interferometric imaging
The standard wide-field imaging technique, the -projection, allows
correction for wide-fields of view for non-coplanar radio interferometric
arrays. However, calculating exact corrections for each measurement has not
been possible due to the amount of computation required at high resolution and
with the large number of visibilities from current interferometers. The
required accuracy and computational cost of these corrections is one of the
largest unsolved challenges facing next generation radio interferometers such
as the Square Kilometre Array. We show that the same calculation can be
performed with a radially symmetric -projection kernel, where we use one
dimensional adaptive quadrature to calculate the resulting Hankel transform,
decreasing the computation required for kernel generation by several orders of
magnitude, whilst preserving the accuracy. We confirm that the radial
-projection kernel is accurate to approximately 1% by imaging the
zero-spacing with an added -term. We demonstrate the potential of our
radially symmetric -projection kernel via sparse image reconstruction, using
the software package PURIFY. We develop a distributed -stacking and
-projection hybrid algorithm. We apply this algorithm to individually
correct for non-coplanar effects in 17.5 million visibilities over a by
degree field of view MWA observation for image reconstruction. Such a
level of accuracy and scalability is not possible with standard -projection
kernel generation methods. This demonstrates that we can scale to a large
number of measurements with large image sizes whilst still maintaining both
speed and accuracy.Comment: 9 Figures, 19 Pages. Accepted to Ap
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
Weighted Mean Curvature
In image processing tasks, spatial priors are essential for robust
computations, regularization, algorithmic design and Bayesian inference. In
this paper, we introduce weighted mean curvature (WMC) as a novel image prior
and present an efficient computation scheme for its discretization in practical
image processing applications. We first demonstrate the favorable properties of
WMC, such as sampling invariance, scale invariance, and contrast invariance
with Gaussian noise model; and we show the relation of WMC to area
regularization. We further propose an efficient computation scheme for
discretized WMC, which is demonstrated herein to process over 33.2
giga-pixels/second on GPU. This scheme yields itself to a convolutional neural
network representation. Finally, WMC is evaluated on synthetic and real images,
showing its superiority quantitatively to total-variation and mean curvature.Comment: 12 page
- …