Research On Algorithm Of Infrared Image Enhancement For Construction Asphalt

The paving of asphalt pavement is very helpful to promote the economic development and improve people’s living standard.During the paving of the asphalt pavement,monitoring the temperature of the asphalt pavement is very important.The accurately controlling the temperature is a key factor in determining the quality of asphalt pavement.If the temperature is too high or too low,the bituminous mixture will be unevenly rolled and the asphalt pavement will be loose and peeling too early.In recent years,infrared camera has many advantages,such as no contact,fast temperature measurement and accurate temperature measurement.It is widely used in the construction of asphalt and it plays a guiding role in the construction of asphalt.However,the surrounding environment during the construction of asphalt is rather harsh and complex,and the collected infrared images are generally affected by many noises,and the collected infrared images become blurred and distorted,thereby reducing the accuracy of image detection and analysis.In order to improve the imaging quality of the infrared camera collected during the construction of asphalt and improve the accuracy of image detection,the acquired of infrared image is enhanced.In order to improve the phenomenon of pseudo-color infrared image distortion caused by construction asphalt,a pseudo-color infrared image enhancement algorithm based on improved wavelet threshold function is proposed in this paper.Due to the discontinuity of the classical wavelet hard threshold function,there is always a fixed deviation between the signal processed by the classical wavelet soft threshold function and the real signal.In order to overcome these shortcomings,this paper improves the threshold function,which is between the soft and hard threshold functions,effectively avoiding the disadvantages of the above two classic threshold functions.After the pseudo-color infrared image is enhanced,the image also contains some noise and blurred details,and it is converted into a gray image to further enhance the processing.This paper proposes an adaptive gray-scale image enhancement algorithm based on Contourlet transform.The algorithm uses the multi-resolution,multi-directionality and near-neighbor sampling of Contourlet transform.And using adaptive genetic algorithm to find the optimal parameters of fuzzy enhancement.The background noise is greatly reduced,the sharpness of the infrared image is improved,the imaging quality of the infrared image is improved,and the accuracy of the temperature monitoring is improved,which plays a good guiding role for the subsequent asphalt construction.Through the MATLAB software,the collected of infrared image of the construction asphalt was experimentally studied.Compared with other classic infrared image enhancement directions,the feasibility and effectiveness of the proposed algorithm were proved.