An image dehazing algorithm based on dark channel correction and weighted aggregate-guided filtering

Abstract:

A new efficient dehazing algorithm is proposed to address the failure of the dark channel prior method in bright areas such as the sky and the blur problem at the edges caused by guided filtering. In this algorithm, a novel adaptive segmentation and correction method is first proposed; the bright areas are segmented based on the relative depth of field threshold, and the value of saturation and grey level are used to calibrate the dark channel in the bright areas. Then, the weighted aggregate guided filtering instead of guided filtering is adopted to refine the initial transmittance to solve the problem of edge blur caused by guided filtering. Finally, an effective brightness correction method is proposed; the restored image is converted to HSV color space to equalize brightness, and the results before and after brightness equalization are linearly weighted to obtain the final result by using relative fog density average as the weigh. The experimental results shows that the proposed algorithm can segment the bright area accurately, restore the image texture clearly and remove the fog thoroughly; the maximum improvement of peak signal to noise ratio, average gradient, information entropy are 34.46%, 99.49%, 21.18%, respectively, by comparing with the previous results.