Author(s)

Ebtesam Mohameed Alharbi^1,2, Hong Wang^1,2*, Peng Ge^1,2

¹School of Electronics and Information, South China University of Technology, Guangzhou, China.
²Engineering Research Center for Optoelectronics of Guangdong Province, School of Science and Opto-Electronics, South China University of Technology, Guangzhou, China.

Low visibility in foggy days results in less contrasted and blurred images with color distortion which adversely affects and leads to the sub-optimal performances in image and video monitoring systems. The causes of foggy image degradation were explained in detail and the approaches of image enhancement and image restoration for defogging were introduced. The study proposed an enhanced and advanced form of the improved Retinex theory-based dehazing algorithm. The proposed algorithm achieved novel in the manner in which the dark channel prior was efficiently combined with the dark-channel prior into a single dehazing framework. The proposed approach performed the first stage in dehazing within the dark channel domain through implementation with an adaptive filter. This novel approach allowed for the dark channel features to be efficiently refined and boosted, a scheme, which according to the obtained results, significantly improved dehazing results in later stages. Experimental results showed that this approach did little to trade-off dehazing speed for efficiency. This makes the proposed algorithm a strong candidate for real-time systems due to its capability to realize efficient dehazing at considerably rapid speeds. Finally, experimental results were provided to validate the superior performance and efficiency of the proposed dehazing algorithm.

Retinex Theory, Dehazing, Image Enhancement and Image Restoration, Image Defogging

Alharbi, E. , Wang, H. and Ge, P. (2017) A Novel Dark-Channel Dehazing Algorithm Based on Adaptive-Filter Enhanced SSR Theory. Journal of Computer and Communications, 5, 60-71. doi: 10.4236/jcc.2017.511005.