Cosmic-Ray Detection Based on Gray-Scale Morphology of Spectroscopic CCD Images

Zhu, Jinkang; Zhu, Zhangqin; Wang, Chong; Ye, Zhongfu

doi:10.1071/as08050

Cited by 7 publications

(1 citation statement)

References 15 publications

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“…As noted in Waters et al (2020) amongst many other imaging works, stacking involves taking multiple exposures of the target and overlaying them. In doing so, it is possible to overlay each exposure to distinguish the target source and the noise source (see also Zhu et al (2009)) and hence remove the noise. Clearly though, for single-shot images or where transient events are of paramount importance, this is not an option.…”

Section: Introductionmentioning

confidence: 99%

Noise reduction in single-shot images using an auto-encoder

Bartlett

Benoit

Pimbblet

et al. 2023

Monthly Notices of the Royal Astronomical Society

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We present an application of autoencoders to the problem of noise reduction in single-shot astronomical images and explore its suitability for upcoming large-scale surveys. Autoencoders are a machine learning model that summarises an input to identify its key features, then from this knowledge predicts a representation of a different input. The broad aim of our autoencoder model is to retain morphological information (e.g., non-parametric morphological information) from the survey data whilst simultaneously reducing the noise contained in the image. We implement an autoencoder with convolutional and maxpooling layers. We test our implementation on images from the Panoramic Survey Telescope and Rapid Response System (Pan-STARRS) that contain varying levels of noise and report how successful our autoencoder is by considering Mean Squared Error (MSE), Structural Similarity Index (SSIM), the second-order moment of the brightest 20% of the galaxy’s flux M20, and the Gini Coefficient, whilst noting how the results vary between original images, stacked images, and noise reduced images. We show that we are able to reduce noise, over many different targets of observations, whilst retaining the galaxy’s morphology, with metric evaluation on a target-by-target analysis. We establish that this process manages to achieve a positive result in a matter of minutes, and by only using one single-shot image compared to multiple survey images found in other noise reduction techniques

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Section: Introductionmentioning

confidence: 99%

Noise reduction in single-shot images using an auto-encoder

Bartlett

Benoit

Pimbblet

et al. 2023

Monthly Notices of the Royal Astronomical Society

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Flux Extraction Based on General Regression Neural Network for Two-Dimensional Spectral Image

Wang

Yin

Guo

et al. 2018

HCI International 2018 – Posters' Extended Abstracts

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Automated Processing of LASCO Coronal Images: Spurious Point-Source-Filtering and Missing-Blocks Correction

Pagot

Lamy²,

Llebaria³

et al. 2013

Sol Phys

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International audienceWe report on automated procedures for correcting the images of the LASCO coronagraph for i) spurious quasi-point-sources such as the impacts of cosmic rays, stars, and planets, and ii) the absence of signal due to transmission errors or dropouts, which results in blocks of missing information in the images. Correcting for these undesirable artifacts is mandatory for all quantitative works on the solar corona that require data inversion and/or long series of images, for instance. The nonlinear filtering of spike noise or point-like objects is based on mathematical morphology and implements the procedure opening by morphological reconstruction. However, a simple opening filter is applied whenever the fractional area of corrupted pixels exceeds 50 % of the original image. We describe different strategies for reconstructing the missing information blocks. In general, it is possible to implement the method of averaged neighbors using the two images obtained immediately before and after the corrupted image. For the other cases, and in particular when missing blocks overlapped in three images, we developed an original procedure of weighted interpolation along radial profiles from the center of the Sun that intercept the missing block(s). This procedure is also adequate for the saturated images of bright planets (such as Venus) that bleed along the neighboring pixels. Missing blocks in polarized images may generally be reconstructed using the associated unpolarized image of the same format. But in the case of overlapping missing blocks, we implemented our procedure of weighted interpolation. All tests performed on numerous LASCO-C2 images at various periods of solar activity (i.e. varying complexity of the structure of the corona) demonstrate the excellent performance of these new procedures, with results vastly superior to the methods implemented so far in the pipeline-processing of the LASCO images

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Cosmic-Ray Detection Based on Gray-Scale Morphology of Spectroscopic CCD Images

Cited by 7 publications

References 15 publications

Noise reduction in single-shot images using an auto-encoder

Noise reduction in single-shot images using an auto-encoder

Flux Extraction Based on General Regression Neural Network for Two-Dimensional Spectral Image

Automated Processing of LASCO Coronal Images: Spurious Point-Source-Filtering and Missing-Blocks Correction

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