Detailed Enhancement of Forward Vehicle Video Images Based on Structured Forest

doi:10.16182/j.issn1004731x.joss.201812014

Abstract

Abstract: In view of the complexity of existing image enhancement methods, which cannot highlight the detection target, this paper proposes a method for detail enhancement of forward vehicle video based on structured forest. This method is mainly divided into two parts: structured forest edge detection and visual enhancement. This paper implements the detail edge extraction and enhancement of the patrolling image of the catenary. The gray histogram distribution after processing is more balanced, and the difference between the average values of gray levels in the detail area and background area of the enhanced patrol image is the standard deviation. The difference is even greater, the peak-to-noise ratio and the structural similarity have been improved. By comparing with other methods, it shows that the algorithm is effective. It can be more intuitive to show railroad workers the anomalous conditions of the railway infrastructure, which is of great practical significance.

Key words: structured forest, edge detection, image enhancement, image fusion

CLC Number:

Jin Weidong, Hu Yanhua, Tang Peng, Li Wei. Detailed Enhancement of Forward Vehicle Video Images Based on Structured Forest[J]. Journal of System Simulation, 2018, 30(12): 4602-4610.

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