Abstract
To process the traffic monitoring image, a local Histogram Equalization method based on fuzzy mathematics was proposed in this paper. In this paper, firstly, we define a function to measure the similarity degree of two images. Then, a suitable Gaussian fuzzy distribution function was chose to generate a 3 × 3 matrix of influential factors. In order to reduce the artificial boundaries, we combined the 3 × 3 influential matrix with a 3 × 3 smooth filter matrix to get the final smooth-influ- ence matrix. Finally, the smooth-influence matrix was used to process the center block image. The simulation results demonstrated that the proposed method can reduce time consumption while improving the image contrast and can get satisfactory results.
Highlights
With the rapid development of IoT (Internet of thing), traffic monitoring technology has been more and more widely used
Histogram equalization can be divided into two categories: one is global histogram equalization; the other is local histogram equalization
When the gray level of the input image gathers in the left part, the gray level of the output image will gather in the right part
Summary
With the rapid development of IoT (Internet of thing), traffic monitoring technology has been more and more widely used. The results showed that BHE method substantially eliminates the excessive bleaching phenomenon of the image, and retains an average luminance; some minor details still have not been enhanced To compensate for this defect, the local histogram equalization came into being. Like AHE method [4], BOHE (over-lapped sub-block histogram equalization) method divides the image into a series of rectangular sub-blocks, and moves the central sub-block one pixel by one pixel. This method can acquire a perfect processed image, but it is too complex to be applied in reality. The rest of this paper is organized as follows: Section 2 is to describe our method, Section 3 is to show the simulation and comparison results, Section 4 is to analyze our method, and Section 5 is to conclude this whole paper
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