The space variant PSF for deconvolution of wide-field astronomical images

2008 ◽  
Author(s):  
M. Řeřábek ◽  
P. Páta
Keyword(s):  
Wide Field ◽  
Astronomical Images ◽  
Space Variant

scholarly journals Space Variant PSF – Deconvolution of Wide-Field Astronomical Images

10.14311/1023 ◽  
2008 ◽  
Vol 48 (3) ◽  
Author(s):  
M. Řeřábek
Keyword(s):  
Optical System ◽  
Imaging System ◽  
Real Data ◽  
Wavefront Aberration ◽  
Wide Field ◽  
Optical Aberrations ◽  
Optical Aberration ◽  
Transfer Characteristics ◽  
Astronomical Images ◽  
Space Variant

The properties of UWFC (Ultra Wide-Field Camera) astronomical systems along with specific visual data in astronomical images contribute to a comprehensive evaluation of the acquired image data. These systems contain many different kinds of optical aberrations which have a negatively effect on image quality and imaging system transfer characteristics, and reduce the precision of astronomical measurement. It is very important to figure two main questions out. At first: In which astrometric depend on optical aberrations? And at second: How optical aberrations affect the transfer characteristics of the whole optical system. If we define the PSF (Point Spread Function) [2] of an optical system, we can use some suitable methods for restoring the original image. Optical aberration models for LSI/LSV (Linear Space Invariant/Variant) [2] systems are presented in this paper. These models are based on Seidel and Zernike approximating polynomials [1]. Optical aberration models serve as suitable tool for estimating and fitting the wavefront aberration of a real optical system. Real data from the BOOTES (Burst Observer and Optical Transient Exploring System) experiment is used for our simulations. Problems related to UWFC imaging systems, especially a restoration method in the presence of space variant PSF are described in this paper. A model of the space variant imaging system and partially of the space variant optical system has been implemented in MATLAB. The “brute force” method has been used for restoration of the testing images. The results of different deconvolution algorithms are demonstrated in this paper. This approach could help to improve the precision of astronomic measurements. 

scholarly journals HST Image Restoration: Current Capabilities and Future Prospects

1994 ◽  
Vol 158 ◽  
pp. 61-69 ◽  
Author(s):  
Robert J. Hanisch ◽  
Richard L. White
Keyword(s):  
Image Restoration ◽  
Dynamic Range ◽  
Hubble Space Telescope ◽  
Spherical Aberration ◽  
Wide Field ◽  
Secondary Mirror ◽  
Faint Object ◽  
Spread Function ◽  
Restoration Algorithms ◽  
Space Variant

The spherical aberration in the primary mirror of the Hubble Space Telescope causes more than 80% of the light from a point source to be spread into a halo of radius of 2–3 arcsec. The point spread function (PSF) is both time variant (resulting from spacecraft jitter and desorption of the secondary mirror support structure) and space variant (owing to the Cassegrain repeater optics in the Wide Field / Planetary Camera). A variety of image restoration algorithms have been utilized on HST data with some success, although optimal restorations require better modeling of the PSF and the development of efficient restoration algorithms that accommodate a spacevariant PSF. The first HST servicing mission (December 1993) will deploy a corrective optics system for the Faint Object Camera and the two spectrographs and a second generation WF/PC with internal corrective optics. As simulations demonstrate, however, the restoration algorithms developed now for aberrated images will be very useful for removing the remaining diffraction features and optimizing dynamic range in post-servicing mission data.

scholarly journals Classification of Planetary Nebulae through Deep Transfer Learning

Galaxies ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 88
Author(s):  
Dayang N. F. Awang Iskandar ◽  
Albert A. Zijlstra ◽  
Iain McDonald ◽  
Rosni Abdullah ◽  
Gary A. Fuller ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Planetary Nebulae ◽  
Parameter Tuning ◽  
Wide Field ◽  
Morphological Classification ◽  
Trade Offs ◽  
Research Platform ◽  
Astronomical Images ◽  
Future Work

This study investigate the effectiveness of using Deep Learning (DL) for the classification of planetary nebulae (PNe). It focusses on distinguishing PNe from other types of objects, as well as their morphological classification. We adopted the deep transfer learning approach using three ImageNet pre-trained algorithms. This study was conducted using images from the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform database (HASH DB) and the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). We found that the algorithm has high success in distinguishing True PNe from other types of objects even without any parameter tuning. The Matthews correlation coefficient is 0.9. Our analysis shows that DenseNet201 is the most effective DL algorithm. For the morphological classification, we found for three classes, Bipolar, Elliptical and Round, half of objects are correctly classified. Further improvement may require more data and/or training. We discuss the trade-offs and potential avenues for future work and conclude that deep transfer learning can be utilized to classify wide-field astronomical images.

scholarly journals Deblending and classifying astronomical sources with Mask R-CNN deep learning

2019 ◽  
Vol 490 (3) ◽  
pp. 3952-3965 ◽  
Author(s):  
Colin J Burke ◽  
Patrick D Aleo ◽  
Yu-Ching Chen ◽  
Xin Liu ◽  
John R Peterson ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Ground Truth ◽  
Wide Field ◽  
Network Architectures ◽  
Deep Imaging ◽  
Learning Technique ◽  
Astronomical Images ◽  
Infrared Survey ◽  
Instance Segmentation

ABSTRACT We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn, is publicly available at https://github.com/burke86/astro_rcnn.

scholarly journals PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems

2017 ◽  
Vol 7 (2) ◽  
pp. 151 ◽  
Author(s):  
Petr Janout ◽  
Petr Páta ◽  
Petr Skala ◽  
Jan Bednář
Keyword(s):  
Field Of View ◽  
Imaging Systems ◽  
Wide Field ◽  
Wide Field Of View ◽  
Space Variant ◽  
Psf Estimation

scholarly journals Advanced Processing of Images Obtained from Wide-field Astronomical Optical Systems

10.14311/1334 ◽  
2011 ◽  
Vol 51 (1) ◽  
Author(s):  
M. Řeřábek ◽  
P. Páta
Keyword(s):  
Image Data ◽  
Optical Systems ◽  
General View ◽  
Wide Field ◽  
Optical Aberrations ◽  
Transfer Characteristics ◽  
Astronomical Image ◽  
Wide Range ◽  
Spread Function ◽  
Space Variant

The principal aim of this paper is to present a general view of the special optical systems used for acquiring astronomical image data, commonly referred to as WFC or UWFC (Ultra Wide Field Camera), and of their transfer characteristics. UWFC image data analysis is very difficult in general, not only because the systems have so-called space variant (SV) properties. Images obtained from UWFC systems are usually incorrectly presented due to a wide range of optical aberrations and distortions. The influence of the optical aberrations increases towards the margins of the field of view. These aberrations distort the point spread function of the optical system and rapidly cut the accuracy of the measurements. This paper deals with simulation and modelling of the UWFC optical systems used in astronomy and their transfer characteristics.

Sign in / Sign up

Export Citation Format

Share Document