This article examines the implications of the application of quantum computing in the field of image processing. A basic conversion associated with gray level processing, such as an image negative, is considered. The article shows how this operation can be expressed using quantum formalism. Whether quantum image processing (in some aspects or some specific applications) has an advantage over classical image processing in realistic scenarios remains to be seen. This depends on solving a number of problems, some of which are unique to quantum image processing, such as feature extraction of a quantum image. Some of them are common to quantum algorithms, such as noise processing. Identifying one of them will significantly speed up the study of this field.

Keywords: qubit, quantum circuit, entanglement, quantum circuit, register, quantum recognition, gate, parallelism, interference, quantum computer

A comparative analysis of the use of distributed and multiprocessor systems of various classes for image processing tasks is considered. Designations and classifications of calculations, the nature of frequently used operations for image processing, the classification of architectures for image processing are given. Issues of correspondence of general classes of computational architectures to types of computational operations used in image processing are investigated. It is shown that each architecture has advantages and disadvantages in some special processing areas, but none of them is optimal for all cases. The most appropriate approach to determining the performance of distributed and multiprocessor systems in this case is the process of their architectural modeling, followed by emulation on the resulting HINT-type test model. The advantages of this approach are the possibility of obtaining a reliable estimate of the performance of the entire system as a whole, as well as the simplicity of its implementation for various architectures.

Keywords: image processing algorithms, distributed systems, multiprocessor computing systems, image analysis, architecture classification