1,259 research outputs found
Fast non-negative deconvolution for spike train inference from population calcium imaging
Calcium imaging for observing spiking activity from large populations of
neurons are quickly gaining popularity. While the raw data are fluorescence
movies, the underlying spike trains are of interest. This work presents a fast
non-negative deconvolution filter to infer the approximately most likely spike
train for each neuron, given the fluorescence observations. This algorithm
outperforms optimal linear deconvolution (Wiener filtering) on both simulated
and biological data. The performance gains come from restricting the inferred
spike trains to be positive (using an interior-point method), unlike the Wiener
filter. The algorithm is fast enough that even when imaging over 100 neurons,
inference can be performed on the set of all observed traces faster than
real-time. Performing optimal spatial filtering on the images further refines
the estimates. Importantly, all the parameters required to perform the
inference can be estimated using only the fluorescence data, obviating the need
to perform joint electrophysiological and imaging calibration experiments.Comment: 22 pages, 10 figure
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
Distributed Deblurring of Large Images of Wide Field-Of-View
Image deblurring is an economic way to reduce certain degradations (blur and
noise) in acquired images. Thus, it has become essential tool in high
resolution imaging in many applications, e.g., astronomy, microscopy or
computational photography. In applications such as astronomy and satellite
imaging, the size of acquired images can be extremely large (up to gigapixels)
covering wide field-of-view suffering from shift-variant blur. Most of the
existing image deblurring techniques are designed and implemented to work
efficiently on centralized computing system having multiple processors and a
shared memory. Thus, the largest image that can be handle is limited by the
size of the physical memory available on the system. In this paper, we propose
a distributed nonblind image deblurring algorithm in which several connected
processing nodes (with reasonable computational resources) process
simultaneously different portions of a large image while maintaining certain
coherency among them to finally obtain a single crisp image. Unlike the
existing centralized techniques, image deblurring in distributed fashion raises
several issues. To tackle these issues, we consider certain approximations that
trade-offs between the quality of deblurred image and the computational
resources required to achieve it. The experimental results show that our
algorithm produces the similar quality of images as the existing centralized
techniques while allowing distribution, and thus being cost effective for
extremely large images.Comment: 16 pages, 10 figures, submitted to IEEE Trans. on Image Processin
An Efficient Approach for Computing Optimal Low-Rank Regularized Inverse Matrices
Standard regularization methods that are used to compute solutions to
ill-posed inverse problems require knowledge of the forward model. In many
real-life applications, the forward model is not known, but training data is
readily available. In this paper, we develop a new framework that uses training
data, as a substitute for knowledge of the forward model, to compute an optimal
low-rank regularized inverse matrix directly, allowing for very fast
computation of a regularized solution. We consider a statistical framework
based on Bayes and empirical Bayes risk minimization to analyze theoretical
properties of the problem. We propose an efficient rank update approach for
computing an optimal low-rank regularized inverse matrix for various error
measures. Numerical experiments demonstrate the benefits and potential
applications of our approach to problems in signal and image processing.Comment: 24 pages, 11 figure
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