383 research outputs found
Fast and easy blind deblurring using an inverse filter and PROBE
PROBE (Progressive Removal of Blur Residual) is a recursive framework for
blind deblurring. Using the elementary modified inverse filter at its core,
PROBE's experimental performance meets or exceeds the state of the art, both
visually and quantitatively. Remarkably, PROBE lends itself to analysis that
reveals its convergence properties. PROBE is motivated by recent ideas on
progressive blind deblurring, but breaks away from previous research by its
simplicity, speed, performance and potential for analysis. PROBE is neither a
functional minimization approach, nor an open-loop sequential method (blur
kernel estimation followed by non-blind deblurring). PROBE is a feedback
scheme, deriving its unique strength from the closed-loop architecture rather
than from the accuracy of its algorithmic components
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
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
Application of Blind Deblurring Reconstruction Technique to SPECT Imaging
An SPECT image can be approximated as the convolution of the ground truth spatial radioactivity with the system point spread function (PSF). The PSF of an SPECT system is determined by the combined effect of several factors, including the gamma camera PSF, scattering, attenuation, and collimator response. It is hard to determine the SPECT system PSF
analytically, although it may be measured experimentally. We formulated a blind deblurring reconstruction algorithm to
estimate both the spatial radioactivity distribution and the system PSF from the set of blurred projection images. The
algorithm imposes certain spatial-frequency domain constraints on the reconstruction volume and the PSF and does
not otherwise assume knowledge of the PSF. The algorithm alternates between two iterative update sequences that
correspond to the PSF and radioactivity estimations, respectively. In simulations and a small-animal study, the algorithm
reduced image blurring and preserved the edges without introducing extra artifacts. The localized measurement shows
that the reconstruction efficiency of SPECT images improved more than 50% compared to conventional expectation
maximization (EM) reconstruction. In experimental studies, the contrast and quality of reconstruction was substantially
improved with the blind deblurring reconstruction algorithm
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