240 research outputs found
Rapid deconvolution of low-resolution time-of-flight data using Bayesian inference
The deconvolution of low-resolution time-of-flight data has numerous advantages, including the ability to extract additional information from the experimental data. We augment the well-known Lucy-Richardson deconvolution algorithm using various Bayesian prior distributions and show that a prior of second-differences of the signal outperforms the standard Lucy-Richardson algorithm, accelerating the rate of convergence by more than a factor of four, while preserving the peak amplitude ratios of a similar fraction of the total peaks. A novel stopping criterion and boosting mechanism are implemented to ensure that these methods converge to a similar final entropy and local minima are avoided. Improvement by a factor of two in mass resolution allows more accurate quantification of the spectra. The general method is demonstrated in this paper through the deconvolution of fragmentation peaks of the 2,5-dihydroxybenzoic acid matrix and the benzyltriphenylphosphonium thermometer ion, following femtosecond ultraviolet laser desorption
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
Semi-Blind Spatially-Variant Deconvolution in Optical Microscopy with Local Point Spread Function Estimation By Use Of Convolutional Neural Networks
We present a semi-blind, spatially-variant deconvolution technique aimed at
optical microscopy that combines a local estimation step of the point spread
function (PSF) and deconvolution using a spatially variant, regularized
Richardson-Lucy algorithm. To find the local PSF map in a computationally
tractable way, we train a convolutional neural network to perform regression of
an optical parametric model on synthetically blurred image patches. We
deconvolved both synthetic and experimentally-acquired data, and achieved an
improvement of image SNR of 1.00 dB on average, compared to other deconvolution
algorithms.Comment: 2018/02/11: submitted to IEEE ICIP 2018 - 2018/05/04: accepted to
IEEE ICIP 201
Least-squares methods with Poissonian noise: an analysis and a comparison with the Richardson-Lucy algorithm
It is well-known that the noise associated with the collection of an
astronomical image by a CCD camera is, in large part, Poissonian. One would
expect, therefore, that computational approaches that incorporate this a priori
information will be more effective than those that do not. The Richardson-Lucy
(RL) algorithm, for example, can be viewed as a maximum-likelihood (ML) method
for image deblurring when the data noise is assumed to be Poissonian.
Least-squares (LS) approaches, on the other hand, arises from the assumption
that the noise is Gaussian with fixed variance across pixels, which is rarely
accurate. Given this, it is surprising that in many cases results obtained
using LS techniques are relatively insensitive to whether the noise is
Poissonian or Gaussian. Furthermore, in the presence of Poisson noise, results
obtained using LS techniques are often comparable with those obtained by the RL
algorithm. We seek an explanation of these phenomena via an examination of the
regularization properties of particular LS algorithms. In addition, a careful
analysis of the RL algorithm yields an explanation as to why it is more
effective than LS approaches for star-like objects, and why it provides similar
reconstructions for extended objects. We finish with a convergence analysis of
the RL algorithm. Numerical results are presented throughout the paper. It is
important to stress that the subject treated in this paper is not academic. In
fact, in comparison with many ML algorithms, the LS algorithms are much easier
to use and to implement, often provide faster convergence rates, and are much
more flexible regarding the incorporation of constraints on the solution.
Consequently, if little to no improvement is gained in the use of an ML
approach over an LS algorithm, the latter will often be the preferred approach.Comment: High resolution images are available upon request. submitted to A&
Adaptive Optimized Discriminative Learning based Image Deblurring using Deep CNN
Image degradation plays a major problem in many image processing applications. Due to blurring, the quality of an image is degraded and there will be a reduction in bandwidth. Blur in an image is due to variations in atmospheric turbulence, focal length, camera settings, etc. Various types of blurs include Gaussian blur, Motion blur, Out-of-focus blur. The effect of noise along with blur further corrupts the captured image. Many techniques have evolved to deblur the degraded image. The leading approach to solve various degraded images are either based on discriminative learning models or on optimization models. Each method has its own advantages and disadvantages. Learning by discriminative methods is faster but restricted to a specific task whereas optimization models handle flexibly but consume more time. Integrating optimization models suitably by learning with discriminative manner results in effective image restoration. In this paper, a set of effective and fast Convolutional Neural Networks (CNNs) are employed to deblur the Gaussian, motion and out-of-focus blurred images that integrate with optimization models to further avoid noise effects. The proposed methods work more efficiently for applications with low-level vision
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