9,539 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
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
Autofocus for digital Fresnel holograms by use of a Fresnelet-sparsity criterion
We propose a robust autofocus method for reconstructing digital Fresnel holograms. The numerical reconstruction
involves simulating the propagation of a complex wave front to the appropriate distance. Since the latter value is difficult to determine manually, it is desirable to rely on an automatic procedure for finding the optimal distance to achieve high-quality reconstructions. Our algorithm maximizes a sharpness metric related to the sparsity of the signal’s expansion in distance-dependent waveletlike Fresnelet bases. We show results from simulations and experimental situations that confirm its applicability
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required [...
Entropy in Image Analysis II
Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas
Extended object reconstruction in adaptive-optics imaging: the multiresolution approach
We propose the application of multiresolution transforms, such as wavelets
(WT) and curvelets (CT), to the reconstruction of images of extended objects
that have been acquired with adaptive optics (AO) systems. Such multichannel
approaches normally make use of probabilistic tools in order to distinguish
significant structures from noise and reconstruction residuals. Furthermore, we
aim to check the historical assumption that image-reconstruction algorithms
using static PSFs are not suitable for AO imaging. We convolve an image of
Saturn taken with the Hubble Space Telescope (HST) with AO PSFs from the 5-m
Hale telescope at the Palomar Observatory and add both shot and readout noise.
Subsequently, we apply different approaches to the blurred and noisy data in
order to recover the original object. The approaches include multi-frame blind
deconvolution (with the algorithm IDAC), myopic deconvolution with
regularization (with MISTRAL) and wavelets- or curvelets-based static PSF
deconvolution (AWMLE and ACMLE algorithms). We used the mean squared error
(MSE) and the structural similarity index (SSIM) to compare the results. We
discuss the strengths and weaknesses of the two metrics. We found that CT
produces better results than WT, as measured in terms of MSE and SSIM.
Multichannel deconvolution with a static PSF produces results which are
generally better than the results obtained with the myopic/blind approaches
(for the images we tested) thus showing that the ability of a method to
suppress the noise and to track the underlying iterative process is just as
critical as the capability of the myopic/blind approaches to update the PSF.Comment: In revision in Astronomy & Astrophysics. 19 pages, 13 figure
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