Improved Image Defogging Algorithm Realization Based On Dark Channel Prior

With scientific progress and development, increasing electronic devices have been widely employed. The information carrier is changed from simple formation of text and audio to images, video for people. Nevertheless, under foggy condition,information such as image or video can be interfered during the information acquisition, which will incur the acquisition of images, make it hard to be identified,and impair the function of systems such as video monitor and object detection.Therefore, attempting to improve image quality, eliminating the positive effect of fog and doing research in dehazing technique can be of significance.Based on the atmospheric scattering model, we analyze the disadvantages in the dark channel dehazing theory and propose an improved algorithm which on image dehazing by using dark channel prior algorithm. The solutions as follows:1) For the dehazing algorithm using dark channel prior, if it were directly applied on images, there would be issues such as distortion of aperture and color spot. In order to solve it, we employ under-estimating transmission to adjust the values of transmission in the distortion area, and make the transmission of this area approach the actual values. Therefore, we solve the distortion problem of bright areas in images.2) For reducing computation time of the algorithm, we utilize different sizes of windows to solve the transmission for image edge and non-edged region. Then the transmissions will be aggregated and put into the dehazing image model for calculating, which avoids large scale matrix operation and significantly improves the processing speed.3) For solving the problem that the entire brightness of restored image is relatively low, we start from HIS color model and make use of Butterworth filtering to enhance the luminance component in images, so that the visual effect we obtain can be better.The experiment demonstrates that the improved algorithm can effectively eliminate the foggy influence, achieve preferable restoration effect and reduce computational time. Finally, we conclude the proposed algorithm and establish a demo system of image dehazing by employing the Microsoft Visual Studio to realize image dehazing, which further shows the effectiveness and practicability of our approach.