An efficient and contrast-enhanced video de-hazing based on transmission estimation using HSL color model

This paper proposed a fast and efficient video de-hazing system with reduced computational complexity for real-time computer vision applications. Video de-hazing is an important task and extensively researched in image/video processing and computer vision. The proposed method initially developed and verified for single images and later extended for real-time video's. The first key aspect of the proposed method is estimating the accurate transmission map using the hue, saturation, and light color model together with red, green, and blue color space. The second relevant aspect is preserving the edges and avoiding halos and artifacts by employing the median of pixels. These aspects reduce the number of computations. It does not require the most computationally complex step of refine transmission map. The advantage of this method is evaluated with five existing classical methods in terms of the average time constant (ATC), peak signal-to-noise ratio, percentage of haze improvement, average contrast of the output image, mean square error and structural similarity index. The comparative experiment shows that the proposed method is two times faster than the existing methods. The qualitative and quantitative analysis demonstrated that the proposed method can attain better de-hazing results and can be efficiently used for real-time video de-hazing applications. Based on comparative analysis, we mapped the proposed method on Raspberry Pi3 and Jetson Nano (GPU) with 24 fps (frames per second) without noticeable delay from input to output and demonstrated for the real-time video.

Automated summary

This paper introduces a fast and efficient video de-hazing system with reduced computational complexity for real-time computer vision applications. It outperforms existing methods by optimizing the transmission map estimation and edge preservation, leading to faster processing speed without sacrificing quality.