The possibility of essentially reducing the weight and production cost of computer vision systems has led to the publication of a large number of research works dealing with the development of new imaging systems based on diffractive optics. This study proposes a new imaging system composed of three diffractive lenses, with each forming a separate channel of the color RGB image. This approach allows us to significantly narrow the spectral range of each lens, thus significantly reducing the image distortion caused by chromatic aberration inherent in diffractive optics. It shows that this scheme allows us to perform the neural network-aided image reconstruction, providing a significantly improved resulting image quality. The study proposes a false edge level criterion (FEL) for evaluating the neural network-aided reconstruction.

Today video analysis is very relevant. Amount of video is being increased and there is a need to perform it. Very often there are a number of video data sources on which video is being continuously recorded. This article presents an implementation of a platform which aggregates video from various sources and processes it with use of different services which are based on artificial neural networks and machine learning methods.

Object classification with use of neural networks is extremely current today. YOLO is one of the most often used frameworks for object classification. It produces high accuracy but the processing speed is not high enough especially in conditions of limited performance of a computer. This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed. Saving efficiency and quality of the neural network work TensorRT allows us to increase the processing speed using an optimization of the architecture and an optimization of calculations on a GPU.