A review on dark channel prior based image dehazing algorithms <s> Transmission map estimation <s> In the frog and haze climatic condition, the captured picture will become blurred and the color is partial gray and white, due to the effect of atmospheric scattering. This situation brings a great deal of inconvenience to the video surveillance system, so the study of defogging algorithm in this weather is of great importance. This paper deeply analyzes the physical process of imaging in foggy weather. After full study on the haze removal algorithm of single image over the last decade, we propo se a fast haze removal algorithm which based on a fast bilateral filtering combined with dark colors prior. This algorithm starts with the atmospheric scattering model, derives a estimated transmission map by using dark channel prior, and then combines with grayscale to extract refined transmission map by using the fast bilateral filter. This algorithm has a fast execution speed and greatly improves the original algorithm which is morre time-consuming. On this basis, we analyzed the reasons why the image is dim after the haze removal using dark channel prior, and proposed the improved transmission map formula. Experimental-results show that this algorithm is feasible which effectively restores the contrast and color of the scene, significantly improves the visual effects of the image. Those image with large area of sky usually prone to distortion when using the dark channel prior, Therefore we propose a method of weakening the sky region, aims to improve the adaptability of the algorithm. <s> BIB001 </s> A review on dark channel prior based image dehazing algorithms <s> Transmission map estimation <s> We propose a single image dehazing method that is based on a physical model and the dark channel prior principle. The selection of an atmospheric light value is directly responsible for the color authenticity and contrast of the resulting image. Our choice of atmospheric light is based on a variogram, which slowly weakens areas in the image that do not conform to the dark channel prior. Additionally, we propose a fast transmission estimation algorithm to shorten the processing time. Along with a subjective evaluation, the image quality was also evaluated using three indicators: MSE, PSNR, and average gradient. Our experimental results show that the proposed method can obtain accurate dehazing results and improve the operational efficiency. <s> BIB002
The transmission mapt x ð Þ fined in Eq. (9) is obtained from the DCP. If the DCP is not exploited, Eq. (9) can be rewritten as As we observed in Section 1.2.2, the pixel value of the dark channel, J dark (x), is highly likely zero, and so is (J/A) dark (x). However, if (J/A) dark (x) is not close to zero, the transmission map obtained as Eq. (9) can be under-estimated since the positive offset in Eq. (16) is always neglected BIB002 . In the original DCP-based dehazing method, it is mentioned that the image may look unnatural if the haze is removed thoroughly  . A constant ω (0 < ω < 1) is thus used to retain a small amount of haze: However, we consider that a better visibility in the dehazed image can be achieved with Eq. (17) because we inadvertently compensate for the under-estimation of t x ð Þ by multiplying ω. (17) by using ω = 0.9, the under-estimation of the transmission map is considerably decreased, as shown in Fig. 10a , c, where the mean values are obtained as 0.5225 and 0.6058, respectively. Xu et al. BIB001 explicitly addressed the aforementioned under-estimation problem of the transmission map and Fig. 7 The average RMSE between the ground-truth (A* = [220,235,254]) and the estimated atmospheric light. The atmospheric light is estimated from pixels with the highest p% dark channel values. Among the p% pixels, the pixel with (a) the highest intensity or (b) the lowest entropy value is used to estimate the atmospheric light. Sixty-six test images from the FRIDA were used (11), making the minimum value of the transmission map be ρ. The under-estimation can be partly solved by using Eq. (17) or (18); however, the values of ω and ρ need to be carefully chosen. To this end, we measured the RMSE values between the ground-truth and estimated transmission maps for different ω and ρ values by using 66 synthetic test images from the FRIDA. Figure 11a , b indicates that ω around 0.9 and ρ around 0.12 are effective. An adaptive scheme also needs to be developed for a better compensation of the under-estimation.