Recently, data mining and neural networks are increasingly used for wavefront recognition from interferograms. In this case, there is considerable freedom in choosing the structure of the reference beam. In this work, a comparative study of the effectiveness of using neural networks for solving the problem of recognizing wavefront aberrations based on linear (flat reference beam) and conical (conical reference wavefront) interferograms is carried out. The effectiveness of recognition of types and levels of aberrations by conical interferograms based on the use of neural networks is shown: the average absolute error is reduced by 3 times, compared with linear interferograms. This effect is related to the rotational invariance of the introduced aberrations.
Synthetic Aperture Radar (SAR) interferometry is an active remote sensing technology that uses microwaves to characterize the earth's surface. SAR interferometry allows to measure the 3D profile of the earth's surface, recover surface topography, and determine topographic displacements over time. The microwave SAR signal is usually highly distorted. Distortions can be caused by, for example, atmospheric disturbances and various characteristics of earth's surface scatterers reflectance. Compensation for these distortions is performed by filtering the phase and evaluating the degree of coherence of the original images. This is an important step to improve the accuracy of the subsequent pphase-unwrapping operation. In this paper, we investigate the use of U-net neural networks for preprocessing the SAR interferogram at various parameters of the distortion of the SAR signal. Two neural networks filter the SAR interferogram and determine the degree of coherence, respectively.
The paper proposes a video surveillance scheme for compact placement of a system for railway rolling stock accounting. This design is based on the use of a tilted diffractive optical element and a tilted lens. Such an optical design makes it possible to significantly increase the depth of focus of the imaging system. This work considers the influence of the tilt of a diffractive lens on the shape and size of the focused area. Analytical relations describing the geometry of the focused region for various spectral channels are given. The possibility of increasing by several times the size of the zone of accurate image classification using a neural network has been demonstrated. The proposed approach has been tested on real-world dataset of images of house number plates.
The paper proposes using a two-zone different level Fresnel lens to increase the depth of field. On the one hand, such a diffractive optical element can reduce the weight of the device compared to, for example, cubic phase and binary axicon apodization. On the other hand, such an element has a simpler structure compared to a harmonic lens or free-form DOE. A neural network is used to restore the image. Optimization of the surface relief of the proposed two-zone lens is performed.
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