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Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras

Zhang, Jiawei, Wang, Xiuyuan, Yang, Chenghai, Zhang, Jian, He, Dongjian, Song, Huaibo
Computers and electronics in agriculture 2018 v.151 pp. 196-206
aircraft, cameras, computer software, entropy, monitoring, remote sensing
Remote sensing technology has been widely used for monitoring crop fields and other agricultural applications. However, the clarity of remote sensing images is often affected by clouds and chaotic media in the atmosphere. Image dehazing can be achieved through the dark channel prior method (DCP), but there is always a brightness distortion problem after image dehazing. To address the problem, this study proposed an improved image dehazing approach based on the DCP method and determined optimal enhancement parameters. Four evaluation indices, including mean square error (MSE), peak signal to noise ratio (PSNR), average gradient and program running time, were first calculated to evaluate the quality of enhanced images. An example image was dehazed by the DCP method initially using the four indices to determine optimal dehazing parameters. Results showed that image enhancement achieved the best effect when the dark channel window size Ω(x) is 5, atmospheric light A is 215/255, and the lower limit t0 of transmission factor t(x) is 0.1. Next, these indices were applied to evaluate the enhancement methods used in this research. The logarithmic enhancement method was finally selected as the optimal method with the base number (1 + r) = 11 and enhancement parameter m = 0.5. To verify the effectiveness of the selected method, 50 airborne images from a consumer-grade camera flown by an agricultural aircraft were used to evaluate the improved method. Both the original and the enhanced images after dehazing were mosaicked by Adobe Photoshop software. The mosaicked images before and after image dehazing were compared. Results showed that the mosaicked image without dehazing had an entropy of 6.359 and an average gradient of 6.513. In comparison, the mosaicked image with dehazing had an entropy of 6.668 and an average gradient of 11.305, which were 4.86% and 73.58% higher than the respective values for the mosaicked image without dehazing. These results indicate that the proposed method in this study is effective and can be applied to dehaze remote sensing images.