2,309 research outputs found
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
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
Morphing Ensemble Kalman Filters
A new type of ensemble filter is proposed, which combines an ensemble Kalman
filter (EnKF) with the ideas of morphing and registration from image
processing. This results in filters suitable for nonlinear problems whose
solutions exhibit moving coherent features, such as thin interfaces in wildfire
modeling. The ensemble members are represented as the composition of one common
state with a spatial transformation, called registration mapping, plus a
residual. A fully automatic registration method is used that requires only
gridded data, so the features in the model state do not need to be identified
by the user. The morphing EnKF operates on a transformed state consisting of
the registration mapping and the residual. Essentially, the morphing EnKF uses
intermediate states obtained by morphing instead of linear combinations of the
states.Comment: 17 pages, 7 figures. Added DDDAS references to the introductio
Scattering and Gathering for Spatially Varying Blurs
A spatially varying blur kernel is specified by an
input coordinate and an output coordinate
. For computational efficiency, we sometimes write
as a linear combination of spatially invariant basis
functions. The associated pixelwise coefficients, however, can be indexed by
either the input coordinate or the output coordinate. While appearing subtle,
the two indexing schemes will lead to two different forms of convolutions known
as scattering and gathering, respectively. We discuss the origin of the
operations. We discuss conditions under which the two operations are identical.
We show that scattering is more suitable for simulating how light propagates
and gathering is more suitable for image filtering such as denoising
Depth and IMU aided image deblurring based on deep learning
Abstract. With the wide usage and spread of camera phones, it becomes necessary to tackle the problem of the image blur. Embedding a camera in those small devices implies obviously small sensor size compared to sensors in professional cameras such as full-frame Digital Single-Lens Reflex (DSLR) cameras. As a result, this can dramatically affect the collected amount of photons on the image sensor. To overcome this, a long exposure time is needed, but with slight motions that often happen in handheld devices, experiencing image blur is inevitable. Our interest in this thesis is the motion blur that can be caused by the camera motion, scene (objects in the scene) motion, or generally the relative motion between the camera and scene. We use deep neural network (DNN) models in contrary to conventional (non DNN-based) methods which are computationally expensive and time-consuming. The process of deblurring an image is guided by utilizing the scene depth and camera’s inertial measurement unit (IMU) records. One of the challenges of adopting DNN solutions is that a relatively huge amount of data is needed to train the neural network. Moreover, several hyperparameters need to be tuned including the network architecture itself.
To train our network, a novel and promising method of synthesizing spatially-variant motion blur is proposed that considers the depth variations in the scene, which showed improvement of results against other methods. In addition to the synthetic dataset generation algorithm, a real blurry and sharp dataset collection setup is designed. This setup can provide thousands of real blurry and sharp images which can be of paramount benefit in DNN training or fine-tuning
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