Video quality enhancement using different enhancement and dehazing techniques

Video in an outdoor environment suffers from the haze problem due to smoke, dust, and other particles in the atmosphere. The quality of video under these atmospheric conditions is clearly/dramatically degraded, leading to poor contrast and information leakage. The proposed algorithm in this paper depends on pre-processing of video frames prior to the dehazing process. Different enhancement techniques are used to remove noise and control the dynamic range, because each frame has some noise due to sensor measurement errors. This noise might be magnified during the haze removal process, if totally neglected. Previous dehazing algorithms enhance the video contrast and reduce frame degradation. We use optimized dehazing algorithms, namely a dual-transmission-map dehazing algorithm, and a recursive Deep Residual Learning (DRL) network, as dehazing tools. We modify the previous dehazing algorithms to generate new algorithms to be suitable not only for visible frames but also for Near Infrared (NIR) frames. In the optimized dehazing algorithms, the haze effect is reduced, according to the hazing model. In the dual-transmission-map dehazing algorithm, the effect of attenuation parameter R and attenuation weight U on the dehazed frames is investigated. A dual-transmission-map algorithm, depending on the Dark Channel Prior (DCP) technique, is studied. We study the effect of a regularization parameter Ω on the visible and NIR dehazed frames without and with enhancement techniques. For the recursive deep residual learning algorithm, we increase the number of iterations in the DRL network from three iterations in the traditional DRL algorithm without denoising techniques to nine iterations, in order to explore the impact of increasing the number of iterations on the output dehazed frames. Since elapsed time grows with the number of iterations, we terminate the operation after the ninth iteration. A comparison between traditional algorithms and the proposed dehazing algorithms is introduced for different types of video considering five frames. In addition, a comparison between previous algorithms and the proposed dehazing algorithms is introduced on real-world hazy images. We use correlation, Peak Signal-to-Noise Ratio (PSNR) between input hazy frame and dehazed one, histogram, and spectral entropy of hazy and dehazed frames as metrics for the proposed algorithms. We conclude that the best dehazing algorithm for the visible videos is the dual-transmission-map dehazing algorithm with enhancement. On the other hand, the best one for NIR videos is the optimized dehazing algorithm with enhancement. For the visible (riverside.avi) video, the enhancement score of the proposed dual-transmission-map dehazing algorithm is 14.74%. It is increased by 9.65% compared to the original dual-transmission-map dehazing algorithm. In contrary, the DRL network achieves a 9% enhancement, which is the lowest one due to the need for a huge training dataset. For NIR video, the enhancement scores of the three different proposed dehazing algorithms, the optimized, the dual-transmission-map, and the DRL network, are 22.28%, 21.57%, and 4.6%, respectively.