Monocular visual pattern processing method

Abstract

Provided is a monocular visual pattern processing method, comprising further adjusting a GA, and allowing an AGA to obtain better universality in various phases of group evolution through correcting a Pc and a Pm; based on a radial basis function neural network algorithm, employing an improved GA to improve a radial basis function neural network to further reduce fault classification problem training errors of the radial basis function neural network algorithm and obtain better training convergence; and finally reconstructing an image according to a super-resolution image reconstruction mathematical processing method to substantially improve the definition of an obtained low-resolution image.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a monocular image processing method. Background technique [0002] The traditional genetic algorithm is also called the standard genetic algorithm (SGA). When applied to the optimal segmentation of images, the advantage is that it can mutate and optimize the target gray level even under complex backgrounds. This method also has some disadvantages, such as: the fixed crossover rate Pc and mutation rate Pm used in the crossover and mutation links can easily lead to too fast convergence of the population, falling into convergence, and insufficient stability. SGA does not make enough use of the feedback information in the system. In the later stage of the optimization process, a large number of unnecessary redundant iterations are required. The efficiency of finding an accurate solution is relatively low, and it is difficult to maintain strong robustness under the premise of...

AI Technical Summary

Problems solved by technology

This method also has some disadvantages, such as: the fixed crossover rate Pc and mutation rate Pm used in the crossover and mutation links can easily lead to too fast convergence of the population, falling into convergence, and insufficient stability

SGA does not make enough use of the feedback information in the system. In the later stage of the optimization process, a large number of unnecessary redundant iterations are required. The efficiency of finding an accurate solution is relatively low, and it is difficult to maintain strong robustness under the premise of a faster convergence speed.

Applied in the field of image processing technology, the processed image is not clear enough

Images