31,408 research outputs found
Phase-and-amplitude recovery from a single phase contrast image using partially spatially coherent X-ray radiation
A simple method of phase-and-amplitude extraction is derived that corrects
for image blurring induced by partially spatially coherent incident
illumination using only a single intensity image as input. The method is based
on Fresnel diffraction theory for the case of high Fresnel number, merged with
the space-frequency description formalism used to quantify partially coherent
fields and assumes the object under study is composed of a single material. A
priori knowledge of the object's complex refractive index and information
obtained by characterizing the spatial coherence of the source is required. The
algorithm was applied to propagation-based phase contrast data measured with a
laboratory-based micro-focus X-ray source. The blurring due to the finite
spatial extent of the source is embedded within the algorithm as a simple
correction term to the so-called Paganin algorithm and is also numerically
stable in the presence of noise
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Purpose: To develop a deep learning approach to de-noise optical coherence
tomography (OCT) B-scans of the optic nerve head (ONH).
Methods: Volume scans consisting of 97 horizontal B-scans were acquired
through the center of the ONH using a commercial OCT device (Spectralis) for
both eyes of 20 subjects. For each eye, single-frame (without signal
averaging), and multi-frame (75x signal averaging) volume scans were obtained.
A custom deep learning network was then designed and trained with 2,328 "clean
B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean
B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance
of the de-noising algorithm was assessed qualitatively, and quantitatively on
1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio
(CNR), and mean structural similarity index metrics (MSSIM).
Results: The proposed algorithm successfully denoised unseen single-frame OCT
B-scans. The denoised B-scans were qualitatively similar to their corresponding
multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR
increased from dB (single-frame) to dB
(denoised). For all the ONH tissues, the mean CNR increased from (single-frame) to (denoised). The MSSIM increased from
(single frame) to (denoised) when compared with
the corresponding multi-frame B-scans.
Conclusions: Our deep learning algorithm can denoise a single-frame OCT
B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior
quality OCT B-scans with reduced scanning times and minimal patient discomfort
Coronal Mass Ejection Detection using Wavelets, Curvelets and Ridgelets: Applications for Space Weather Monitoring
Coronal mass ejections (CMEs) are large-scale eruptions of plasma and
magnetic feld that can produce adverse space weather at Earth and other
locations in the Heliosphere. Due to the intrinsic multiscale nature of
features in coronagraph images, wavelet and multiscale image processing
techniques are well suited to enhancing the visibility of CMEs and supressing
noise. However, wavelets are better suited to identifying point-like features,
such as noise or background stars, than to enhancing the visibility of the
curved form of a typical CME front. Higher order multiscale techniques, such as
ridgelets and curvelets, were therefore explored to characterise the morphology
(width, curvature) and kinematics (position, velocity, acceleration) of CMEs.
Curvelets in particular were found to be well suited to characterising CME
properties in a self-consistent manner. Curvelets are thus likely to be of
benefit to autonomous monitoring of CME properties for space weather
applications.Comment: Accepted for publication in Advances in Space Research (3 April 2010
Surface networks
© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou
Performance of the Gemini Planet Imager Non-Redundant Mask and spectroscopy of two close-separation binaries HR 2690 and HD 142527
The Gemini Planet Imager (GPI) contains a 10-hole non-redundant mask (NRM),
enabling interferometric resolution in complement to its coronagraphic
capabilities. The NRM operates both in spectroscopic (integral field
spectrograph, henceforth IFS) and polarimetric configurations. NRM observations
were taken between 2013 and 2016 to characterize its performance. Most
observations were taken in spectroscopic mode with the goal of obtaining
precise astrometry and spectroscopy of faint companions to bright stars. We
find a clear correlation between residual wavefront error measured by the AO
system and the contrast sensitivity by comparing phase errors in observations
of the same source, taken on different dates. We find a typical 5-
contrast sensitivity of at . We explore the
accuracy of spectral extraction of secondary components of binary systems by
recovering the signal from a simulated source injected into several datasets.
We outline data reduction procedures unique to GPI's IFS and describe a newly
public data pipeline used for the presented analyses. We demonstrate recovery
of astrometry and spectroscopy of two known companions to HR 2690 and HD
142527. NRM+polarimetry observations achieve differential visibility precision
of in the best case. We discuss its limitations on
Gemini-S/GPI for resolving inner regions of protoplanetary disks and prospects
for future upgrades. We summarize lessons learned in observing with NRM in
spectroscopic and polarimetric modes.Comment: Accepted to AJ, 22 pages, 14 figure
Improved Stroke Detection at Early Stages Using Haar Wavelets and Laplacian Pyramid
Stroke merupakan pembunuh nomor tiga di dunia, namun hanya sedikit metode tentang deteksi dini. Oleh karena itu dibutuhkan metode untuk mendeteksi hal tersebut. Penelitian ini mengusulkan sebuah metode gabungan untuk mendeteksi dua jenis stroke secara simultan. Haar wavelets untuk mendeteksi stroke hemoragik dan Laplacian pyramid untuk mendeteksi stroke iskemik. Tahapan dalam penelitian ini terdiri dari pra proses tahap 1 dan 2, Haar wavelets, Laplacian pyramid, dan perbaikan kualitas citra. Pra proses adalah menghilangkan bagian tulang tengkorak, reduksi derau, perbaikan kontras, dan menghilangkan bagian selain citra otak. Kemudian dilakukan perbaikan citra. Selanjutnya Haar wavelet digunakan untuk ekstraksi daerah hemoragik sedangkan Laplacian pyramid untuk ekstraksi daerah iskemik. Tahapan terakhir adalah menghitung fitur Grey Level Cooccurrence Matrix (GLCM) sebagai fitur untuk proses klasifikasi. Hasil visualisasi diproses lanjut untuk ekstrasi fitur menggunakan GLCM dengan 12 fitur dan kemudian GLCM dengan 4 fitur. Untuk proses klasifikasi digunakan SVM dan KNN, sedangkan pengukuran performa menggunakan akurasi. Jumlah data hemoragik dan iskemik adalah 45 citra yang dibagi menjadi 2 bagian, 28 citra untuk pengujian dan 17 citra untuk pelatihan. Hasil akhir menunjukkan akurasi tertinggi yang dicapai menggunakan SVM adalah 82% dan KNN adalah 88%
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