Objective

2

Many image processing models and algorithms in the field of image processing exist at present. Retinex algorithms and multifocus image fusion algorithms in the field have become the most widely used image processing algorithms because of their simplicity

efficiency

and easy implementation. However

Retinex algorithms and multifocus image fusion algorithms have limitations on image processing. This study aims to combine the algorithm with the ideas of Retinex and multifocus image fusion algorithms to achieve image enhancement. In view of the traditional Retinex algorithms

graying

halo

and boundary phenomena are considered

as well as the problem in which the details of high-exposure area is not enhanced. The traditional multifocus image fusion algorithms must capture multiple images of different focus points in the same scene

extract the focus area from multiple images of different focus points

and operate the focus area for multifocus fusion. Extracting the repeated operations of multiple image focus areas for the multifocus fusion algorithm is necessary. This study proposes a multifocus image fusion algorithm based on Retinex the improved bilateral filtering of Retinex.

Method

2

The Retinex algorithm is used to improve the process of estimating illumination images. The effects of image space proximity similarity and pixel similarity on the enhancement effect are fully considered. The improved kernel function of the traditional Retinex algorithm is enhanced to obtain an improved bilateral filter function. First

the improved bilateral filtering function is used to estimate the illumination image

and the influence of the illumination image on the visual effect is reduced or suppressed

and then the reflected image of the essence of the response image is obtained. Furthermore

the optimal parameters of the optimal bright and dark regions are calculated at the pixel level of the reflected image

and the optimal parameters are brought into the algorithm to find the optimal solution. The optimal solution can be used to decompose the reflected image into the optimal bright region and the optimal dark area. In combination with the idea of multifocus image fusion algorithms

the original image is introduced at the same time

which can ensure the enhancement effect of the image and preserve the detail and quality in the original image

so that the enhancement effect is more obvious and clear. The original image

optimal bright region image

and optimal dark region image are multifocus fusion

and the linear integral transform and adjacent pixel optimal recommendation algorithm are used to obtain a smooth and accurate steering map. The steering filter is used to further smooth the steering map. Using the processed guide map as a basis

the original image and optimal bright region are fused according to different focal lengths to obtain an image

T

. The image

T

is repeated with the optimal dark region image to obtain the process image

S

by the aforementioned experimental steps and finally utilized. The pilot filter performs boundary repair on the process image

S

to obtain a final result image.

Result

2

Pictures of a girl and a boat

which were selected for comparison experiments

with single-scale Retinex algorithm

bilateral filter-based Retinex algorithm

BIFT algorithm (Retinex image enhancement algorithm based on image fusion technology)

and RVRG algorithm (Retinex variational model based on relative gradient regularization and its application). The proposed algorithm shows obvious advantages in variance and information entropy

specifically with the average of 16.37 higher than that of BIFT

and the average is increased by 20.90 compared with RVRG. The average increase is 1.25 higher compared with BIFT in terms of variance

which is an average increase of 4.42 compared with that of RVRG. In terms of information entropy

the average increase is 0.1 compared with that of BIFT

which is an average increase of 0.17 compared with that of RVRG. In terms of average gradient

the average increase was 1.21 compared with that of BIFT

which was an average increase of 0.42 compared with that of RVRG. Experimental data comparing BIFT and RVRG demonstrate the effectiveness of the proposed algorithm. The details in the original image and the dark portion of the right side of the car body in the image of the girl are significantly enhanced

and the details of the bright areas in the original image are well-preserved while being enhanced. The overall pixel value of the image is low and the visual effect is darker in the image of the boat. After the enhancement

the outline of the character is clear

the water surface is distinct

and the reef texture enhancement effect is remarkable.

Conclusion

2

The extensive experiments show that due to the uncertainty of the process of acquiring images and the complexity of image information

the traditional algorithms are inevitably limited and the enhancement effect cannot meet the high-quality requirements of image preprocessing. Compared with the traditional algorithms

the combination of Retinex and multifocus fusion algorithms proposed in this paper has a better image enhancement effect. Compared with the single-scale Retinex

bilateral filter-based Retinex

BIFT

and RVRG

the reconstructed image is superior to the contrast algorithm at the subjective and objective levels. The objective evaluation parameters of the resulting image are greatly improved

especially in terms of information entropy. The algorithm in this paper can effectively suppress image graying

halo

and boundary prominent phenomena. The contrast is significantly enhanced and has a good enhancement effect on uneven illumination images and low contrast images

making it superior to other contrast algorithms in image enhancement and robustness. The image detail enhancement effect is particularly remarkable because it serves as the foundation for the subsequent image processing.