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ARS Home » Plains Area » College Station, Texas » Southern Plains Agricultural Research Center » Aerial Application Technology Research » Research » Publications at this Location » Publication #347615
Title: Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring

Author
item WANG, XIUYUAN - Northwest Agricultural & Forestry University
item Yang, Chenghai
item JIAN, ZHANG - Huazhong Agricultural University
item SONG, HUAIBO - Northwest Agricultural & Forestry University

Submitted to: International Journal of Agricultural and Biological Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/17/2018
Publication Date: 6/25/2018
Citation: Wang, X., Yang, C., Jian, Z., Song, H. 2018. Image dehazing based on dark channel prior and brightness enhancement for agricultural monitoring. International Journal of Agricultural and Biological Engineering. 11(2):170-176.

Interpretive Summary: Under the influence of fog, haze and other adverse weather conditions, remotely sensed images are usually blurred and distorted making them unsuitable for image processing. Traditional image dehazing methods can cause the loss of image quality and cause image distortion. This study developed an improved image dehazing method, which enhanced the brightness of the hazed image and adjusted the brightness of the sky area. The method was compared with two commonly-used image dehazing methods, and image evaluation indicators showed that the method performed better than the two traditional methods. This study provided a useful image dehazing tool for agricultural field monitoring.

Technical Abstract: Obtaining clear and true images is a basic requirement for agricultural monitoring. However, under the influence of fog, haze and other adverse weather conditions, captured images are usually blurred and distorted, resulting in the difficulty of target extraction. Traditional image dehazing methods based on image enhancement technology can cause the loss of image information and image distortion. The primary goal of this study was to address the above-mentioned problems caused by traditional image dehazing methods. An improved image dehazing method based on dark channel prior (DCP) was proposed. By enhancing the brightness of the hazed image and processing the sky area, the dim and un-natural problems caused by traditional image dehazing algorithms were resolved. In order to verify the effectiveness of the proposed algorithm, a total of 20 different test image groups selected from different weather conditions were used, and the algorithm was compared with the commonly-used histogram equalization algorithm and the DCP method. Three image evaluation indicators including mean square error (MSE), peak signal to noise ratio (PSNR), and entropy were used to evaluate the dehazing performance. Results showed that the proposed method increased PSNR by 22.0% and entropy by 4.9% and decreased MSE by 38.3% compared with the original DCP method. It performed much better than the histogram equalization dehazing method with an increase of PSNR by 51.0% and entropy by 23.0% and a decrease of MSE by 87.7%. The results from this study will provide useful information for agricultural field monitoring.