Rapid image repairing method based on rough set

Abstract

The invention discloses a rapid image repairing method based on a rough set. The rapid image repairing method comprises the following steps: establishing a rough set model for an acquired digital image, and abstracting a knowledge representation system; then performing brightness grade division according to an equivalence relation in the knowledge representation system; solving illumination brightness layers of a discourse domain (an image) through upper approximation and lower approximation according to the division; then introducing approximate classification accuracy and system parameter importance, and calculating the variation trend of a value along with the division layer number; finally, feeding back optimal division according to the convergence of the approximate classification accuracy and the system parameter importance so as to self-adapt to the brightness layers, dividing all the brightness layers into an excessive region, a normal region and an over-dark region, and restoring the brightness layer by layer by using the normal region as the reference. A clear image with high visibility and outstanding details is obtained finally. The rapid image repairing method is high in adaptability and high in processing speed, has certain online timeliness, is of great realistic significance, and has a great practical value; various severe illumination environment conditions can be overcome.

Description

technical field [0001] The invention belongs to the field of machine vision detection, is an application of image processing and recognition technology, and in particular relates to a rough set-based rapid image repair method. Background technique [0002] In many actual imaging environments, the lighting conditions are often not up to the ideal level, reflected in the image is too bright or too dark blocks or stripes, which seriously interfere with the analysis and judgment of the image, so how to effectively and quickly remove shadows has a wide range application prospects. [0003] Acquiring images under complex environmental conditions often has the following difficulties: the influence of the propagation medium, due to the turbidity of the medium, the absorption, attenuation and scattering of light by suspended particles in the medium, etc., will cause the quality of the collected image to degrade; the light source is not ideal, In some conditions where natural light c...

AI Technical Summary

Problems solved by technology

This type of algorithm has high computational complexity, which is not conducive to practical application

[0005] Although the above solutions can uniformly illuminate to a certain extent, there is still a lack of rapid uniform illumination under complex environmental conditions.

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