Colorization is a process of converting grayscale images into visually acceptable color images. The main goal is to convince the viewer of the authenticity of the result. Grayscale images that need to be colorized are, in most cases, images with natural scenes. Over the last 20 years a wide range of colorization methods has been developed – from algorithmically simple, yet time- and energy-consuming because of unavoidable human intervention to more complicated, but simultaneously more automated methods. Automatic conversion has become a challenging area that combines machine learning and deep learning with art. This paper presents an overview and evaluation of grayscale image colorization methods and techniques applied to natural images. The paper provides a classification of existing colorization methods, explains the principles on which they are based, and highlights their advantages and disadvantages. Special attention is paid to deep learning methods. Relevant methods are compared in terms of image quality and processing time. Different metrics for color image quality assessment are used. Measuring the perceived quality of a color image is challenging due to the complexity of the human visual system. Multiple metrics used to evaluate colorization methods provide results by determining the difference between the predicted color value and the ground truth, which in several cases is not in coherence with image plausibility. The results show that user-guided neural networks are the most promising category for colorization because they successfully combine human intervention and neural network automation.

Recently, several applications leveraging unconventional manipulation of electromagnetic radiation based on parity-time symmetry have been proposed in the literature. Typical examples include systems with unidirectional invisibility and asymmetric refraction. Such applications assume an inherent system stability and no occurrence of unbounded signal growth or unwanted self-oscillations. Here, a general instability issue of parity-time-symmetric systems is investigated, with particular emphasis on a recently proposed system based on resistive metasurfaces. Explicit closed-form stability criterion is derived, crosschecked and verified by both time-domain transient simulations and the measurements on an experimental demonstrator operating in a lower radiofrequency range. Results of this study lead to the conclusion that any parity-time-symmetric system is necessarily marginally stable. Finally, it is shown that such a marginally stable system may easily become unstable if not designed carefully.

Global navigation satellite systems' performance is affected by the ionospheric signal delay. Ionospheric models are continuously enhanced to better describe ionospheric dynamics and reduce positioning errors. Changes in the geomagnetic field, influenced by the interplanetary magnetic field, reflect to the amount and distribution of the ionospheric ionization, quantified with the Total Electron Content (TEC). This paper characterizes the ionosphere over Europe during a geomagnetic storm. Combined information of temporal TEC change and spatial TEC gradients provides better overview of the ionospheric structures and enables clear insight in their dynamics and evolution.

The differential interferometric synthetic aperture radar (DInSAR) method is based on phase variation between the complex value of pixels of timely separated scenes in interferometric SAR pairs. This phase variation has five components: surface topography, curvature of planet’s surface, terrain displacement, volume scatterers, and atmospheric propagation effects. The terrain displacement is the main product of the DInSAR method, while the last two effects are unpredictable and bring inaccuracy into the terrain displacement measurements. In this work, the propagation conditions in the troposphere and ionosphere were studied during two DInSAR measurements examining the Zagreb 22 March 2020 earthquake, with terrain raising of up to +3 cm at the epicenter. For the troposphere, the vertical profile of the modified refraction index, which incorporates local curvature change with height, was reconstructed using aerological balloon probing data. Ionospheric conditions were determined based on total electron content (TEC) calculated from the Croatian positioning system (CROPOS) and global navigation satellite system (GNSS) reference stations’ measurements. One of the DInSAR measurements was conducted in unfavorable tropospheric refractive conditions, which resulted in an overall bias of −2 cm. The variability of propagation conditions indicates the need for examining the atmospheric propagation effects when calculating terrain displacements using the DInSAR method. The results of DInSAR indicate slight displacements, comparable with the amplitude of atmospheric variations, and should therefore be approached with caution.