Adaptive brightness unevenness variation correction method based on variation framework

Abstract

The invention discloses an adaptive brightness unevenness variation correction method based on a Retinex variation framework and aims to provide an adaptive correction method to realize no logarithm transformation, eliminate a non-uniform image brightness phenomenon and effectively keep image colors and the detailed information. The method is characterized in that an optical imaging detection system is utilized to acquire an uneven degeneration image as an observation image; an adaptive weight function is constructed according to space characteristics of the observation image; an original Retinex theory is utilized to construct a variation correction model, total variation regularization constraint intensities of a reflection component in the variation correction model at different pixel points are adaptively controlled through the weight function, and a mean value of the reflection component is employed to approximate gray mean constraints to prevent local overexposure; a split Bregman iteration method and an alternative minimization method are then utilized to solve the variation correction model to acquire an illumination component and the reflection component, and the reflection component is lastly rounded to acquire a correction image.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and in particular relates to an adaptive brightness uneven variation correction method based on a Retinex variation framework. Among them, the Retinex variational framework means to use the variational method to solve the Retinex theory, which is commonly used in uniform light variational image processing. Background technique [0002] With the rapid development of computer technology and image processing technology, image / video-based working systems have penetrated into all walks of life, such as target recognition, automatic navigation, intelligent monitoring, terrain survey, automatic driving and civilian shooting and other fields. , has played a role that cannot be ignored in national economic and social development. However, in the imaging process of the sensor, due to the influence of environmental factors such as atmosphere and light as well as the internal factors of the...

AI Technical Summary

Problems solved by technology

Therefore, after the logarithmic transformation of the observed image, the edge and detail information of the bright area will be blurred and lost, and an important information contained in the image reflectance is the edge information in the image, so the Retinex transformation established based on the logarithmic transformation The homogenization correction image calculated by the sub-calibration model will have blurred edges and loss of details, such as the TVRE method proposed by Michael et al. and the SARV method proposed by Lan Xia et al., although better dodging correction can be obtained compared to other methods However, since the logarithmic transformation of the observed image will blur and lose the edge and detail information of the bright area, the dodging correction image also has the phenomenon of blurred edges and loss of details ( Figure 4 c,4d, Figure 5 c,5d, Image 6 c,6d, Figure 7 b,7c, Figure 8 b,8c, Figure 9 b, 9c, Figure 10 b, 10c)

Images