A review on dark channel prior based image dehazing algorithms <s> Cross-bilateral filter <s> In this paper, we propose a simple but effective image prior - dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of the haze-free outdoor images. It is based on a key observation - most local patches in haze-free outdoor images contain some pixels which have very low intensities in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high quality haze-free image. Results on a variety of outdoor haze images demonstrate the power of the proposed prior. Moreover, a high quality depth map can also be obtained as a by-product of haze removal. <s> BIB001 </s> A review on dark channel prior based image dehazing algorithms <s> Cross-bilateral filter <s> Outdoor photography and computer vision tasks often suffer from bad weather conditions, observed objects lose visibility and contrast due to the presence of atmospheric haze, fog, and smoke. In this paper, we propose a new method for real-time image and video dehazing. Based on a newly presented haze-free image prior - dark channel prior and a common haze imaging model, for a single input image, we can estimate the global atmospheric light and extract the scene objects transmission. To prevent artifacts, we refine the transmission using a cross-bilateral filter, and finally the haze-free frame can be restored by inversing the haze imaging model. The whole process is highly parallelized, and can be easily implemented on modern GPUs to achieve real-time performance. Comparing with existing methods, our approach provides similar or better results with much less processing time. The proposed method can be further used for many applications such as outdoor surveillance, remote sensing, and intelligent vehicles. In addition, rough depth information of the scene can be obtained as a by-product. <s> BIB002
The cross-bilateral filter (aka joint-bilateral filter) is a variant of the classic bilateral filter. Unlike the bilateral filter, the cross-bilateral filter computes the range kernel from a cross (guidance) channel as follows: where the guidance channel I corresponds to the hazy image as in Eq. (1). Therefore, the sharpness of I can be inherited to the transmission mapt. Figure 18 shows the result of the cross-bilateral filter, and Fig. 19 shows the quantitative performance evaluation result using a fixed value of σ s = 15 and various σ r values. Owing to the use the cross channel, the resultant transmission map can Fig. 16 The result of the soft matting using λ = 10 − 4 . a Hazy images. b Transmission maps obtained using the local patch with the size 3 × 3. c Soft matting results of (b). d Transmission maps obtained using the local patch with the size 15 × 15. e Soft matting results of (d) exhibit sharper edges than those obtained by the Gaussian and bilateral filters. The selection of σ r was also found to be important for the accuracy of the transmission map, and the best value of σ r was found around 0.1 regardless of the size of the local patch. Unlike the computationally expensive soft matting method (which takes 10-20 s on average for images with the size 600 × 400 BIB001 ), it was shown in BIB002 that the cross-bilateral filter can be implemented in real-time using the GPU.