Haze detection algorithm based on image energy and contrast

In view of the poor real-time performance and high cost of haze detection methods, a method based on contrast and image energy was proposed to detect the haze. Firstly, the images taken by the CMOS camera were preprocessed. The image has some slight swing because the camera has been disturbed by the external environment, so the images were registered. Secondly, in the critical region of the image, two contrast vectors of contrast and image energy were obtained. Thirdly, the contrast, image energy and ambient humidity were taken as input, and the real-time PM10 concentration measured by the laser particle counter was used as the output. The relational model between input and output was constructed by training support vector regression(SVR). Finally, the PM10 concentration of the image was calculated using the model. The PM10 concentration detected by this method was compared with that measured by laser particle counter. The average relative error was less than 10% and MSE was 0.006 2, which indicates that the fitting degree between the predicted value and the true value is good and the accuracy of the model was high. On this basis, increasing the training samples can improve the model accuracy. Moreover, the method can establish the corresponding relation model for different environment to be tested, which has strong flexibility.