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The BRightest Target Explorer (BRITE) is the pioneering nanosatellite mission dedicated for photometric observations of the brightest stars in the sky. The BRITE charge coupled device (CCD) sensors are poorly shielded against extensive flux of energetic particles which constantly induce defects in the silicon lattice. In this paper we investigate the temporal evolution of the generation of the dark current in the BRITE CCDs over almost four years after launch. Utilizing several steps of image processing and employing normalization of the results, it was possible to obtain useful information about the progress of thermal activity in the sensors. The outcomes show a clear and consistent linear increase of induced damage despite the fact that only about 0.14% of CCD pixels were probed. By performing the analysis of temperature dependencies of the dark current, we identified the observed defects as phosphorus-vacancy (PV) pairs, which are common in proton irradiated CCD matrices. Moreover, the Meyer-Neldel empirical rule was confirmed in our dark current data, yielding E M N = 24.8 meV for proton-induced PV defects.

Imaging through turbulence has been the subject of many research papers in a variety of fields, including defence, astronomy, earth observations, and medicine. The main goal of such research is usually to recover the original, undisturbed image, in which the impact of spatially dependent blurring induced by the phase modulation of the light wavefront is removed. The number of turbulence-disturbed image databases available online is small, and the datasets usually contain repeating types of ground objects (cars, buildings, ships, chessboard patterns). In this article, we present a database of solar images in widely varying turbulence conditions obtained from the SUTO-Solar patrol station recorded over a period of more than a year. The dataset contains image sequences of distinctive yet randomly selected fragments of the solar chromosphere and photosphere. Reference images have been provided with the data using computationally intensive image recovery with the latest multiframe blind deconvolution technique, which is widely accepted in solar imaging. The presented dataset will be extended in the next few years as new image sequences are routinely acquired each sunny day at the SUTO-Solar station.

Heliophysics phenomena on the Sun, such as radio bursts, can strongly affect satellites and ground-based electronic systems. Therefore, an insight into the actual image of the Sun with good spatial and temporal resolution is crucial. In this paper, we explore the possibility of using fully convolutional networks (FCNs) to improve the images acquired from remotely operated small solar telescopes whose resolution is limited by the size of the lens aperture and by atmospheric turbulence. For this purpose, we use chromosphere data from the 50 mm small H Telescope of the Silesian University of Technology acquired over many months under various atmospheric conditions. We compare the obtained results with the results of raw data processing by a state-of-the-art deterministic algorithm, multi-frame blind deconvolution (MFBD). In our research, we investigate the impact of the amount of data and the complexity of FCNs on the quality of the results and their processing time. We show that the use of FCNs is a very attractive alternative to MFBD because they are more energy efficient and allow for the obtaining of comparable results in orders of magnitude shorter time.

Personal identification using analysis of the internal and external characteristics of the human finger is currently an intensively developed topic. The work in this field concerns new methods of feature extraction and image analysis, mainly using modern artificial intelligence algorithms. However, the quality of the data and the way in which it is obtained determines equally the effectiveness of identification. In this article, we present a novel device for extracting vision data from the internal as well as external structures of the human finger. We use spatially selective backlight consisting of NIR diodes of three wavelengths. The fast image acquisition allows for insight into the pulse waveform. Thanks to the external illuminator, images of the skin folds of the finger are acquired as well. This rich collection of images is expected to significantly enhance identification capabilities using existing and future classic and AI-based computer vision techniques. Sample data from our device, before and after data processing, have been shared in a publicly available database.

Deep neural networks (DNNs) have achieved outstanding results in domains such as image processing, computer vision, natural language processing and bioinformatics. In recent years, many methods have been proposed that can provide a visual explanation of decision made by such classifiers. Saliency maps are probably the most popular. However, it is still unclear how to properly interpret saliency maps for a given image and which techniques perform most accurately. This paper presents a methodology to practically evaluate the real effectiveness of saliency map generation methods. We used three state-of-the-art network architectures along with specially prepared benchmark datasets, and we proposed a novel metric to provide a quantitative comparison of the methods. The comparison identified the most reliable techniques and the solutions which usually failed in our tests.

Dark current in charge-coupled devices (CCDs) is one of the most important sources of impulsive noise present in scientific images. While the dark current originating in the fabrication defects (mainly impurities) is stable and dependent only on temperature, the one present in the proton-irradiated sensors shows a range of metastable states which makes calibration of images almost impossible. In this paper, we show an extended analysis of such metastabilities present in Kodak KAI 11000M CCD sensors employed in the BRITE (BRIghtest Target Explorer) astrophysical mission over 7 years of in-orbit work. Our collection of dark current characteristics has an unprecedented time span, large temperature range and high number of investigated pixels. A special methodology based on the Gaussian mixture model was proposed for identification and characterization of the metastable states in the dark current. We identified several interesting properties of the metastability and found an experimental rule for the dark current in tristable defects. The results shed a new light on the dark current problems, its modeling and the mitigation in an image sensor working in space.

Most of the current image processing methods used in the near-infrared imaging of finger vascular system concentrate on the extraction of internal structures (veins). In this paper, we propose a novel approach which allows to enhance both internal and external features of a finger. The method is based on the Distance Transformation and allows for selective extraction of physiological structures from an observed finger. We evaluate the impact of its parameters on the effectiveness of the already established processing pipeline used for biometric identification. The new method was compared with five state-of-the-art approaches to features extraction (position-gray-profile-curve—PGPGC, maximum curvature points in image profiles—MC, Niblack image adaptive thresholding—NAT, repeated dark line tracking—RDLT, and wide line detector—WD) on the GustoDB database of images obtained in a wide range of NIR wavelengths (730–950 nm). The results indicate a clear superiority of the proposed approach over the remaining alternatives. The method managed to reach over 90 % identification accuracy for all analyzed datasets.

Monochromatic images are used mainly in cases where the intensity of the received signal is examined. The identification of the observed objects as well as the estimation of intensity emitted by them depends largely on the precision of light measurement in image pixels. Unfortunately, this type of imaging is often affected by noise, which significantly degrades the quality of the results. In order to reduce it, numerous deterministic algorithms are used, with Non-Local-Means and Block-Matching-3D being the most widespread and treated as the reference point of the current state-of-the-art. Our article focuses on the utilization of machine learning (ML) for the denoising of monochromatic images in multiple data availability scenarios, including those with no access to noise-free data. For this purpose, a simple autoencoder architecture was chosen and checked for various training approaches on two large and widely used image datasets: MNIST and CIFAR-10. The results show that the method of training as well as architecture and the similarity of images within the image dataset significantly affect the ML-based denoising. However, even without access to any clear data, the performance of such algorithms is frequently well above the current state-of-the-art; therefore, they should be considered for monochromatic image denoising.

This article introduces an innovative meteor detection system that integrates high-speed photodiode detectors with traditional camera-based systems. The system employs four photodiodes to record changes in sky brightness at 100 Hz, enabling meteor detection and the observation of their dynamics. This technology serves as a valuable complement to existing imaging techniques, offering a cost-effective solution for measuring meteor ablation at frequencies beyond the capabilities of camera-based systems. We showcase findings from the Perseid meteor shower, demonstrating the potential of our system. Moreover, our system addresses the current limitations in meteor radiometry, where many existing instruments either remain in developmental stages or have not been validated with a substantial number of confirmed meteor events. Our approach successfully addresses these limitations, demonstrating effectiveness across multiple meteor events simultaneously recorded on video.

Deep neural networks (DNNs) have been used successfully for many image classification problems. One of the most important factors that determines the final efficiency of a DNN is the correct construction of the training set. Erroneously labeled training images can degrade the final accuracy and additionally lead to unpredictable model behavior, reducing reliability. In this paper, we propose MultiNET, a novel method for the automatic detection of noisy labels within image datasets. MultiNET is an adaptation of the current state-of-the-art confident learning method. In contrast to the original, our method aggregates the outputs of multiple DNNs and allows for the adjustment of detection sensitivity. We conduct an exhaustive evaluation, incorporating four widely used datasets (CIFAR10, CIFAR100, MNIST, and GTSRB), eight state-of-the-art DNN architectures, and a variety of noise scenarios. Our results demonstrate that MultiNET significantly outperforms the confident learning method.