Image de-hazing from the perspective of noise filtering. (August 2017)

• Record Type: Journal Article
• Authors: Liu, Shilong; Rahman, Md Arifur; Liu, San Chi; Yeow Wong, Chin; Lin, Ching-Feng; Wu, Hongkun; Kwok, Ngaiming
• Is Part Of: Computers & electrical engineering. Volume 62(2017)
• Page Start: 345
• Page End: 359
• Publication Date: 2017-08
• Subjects: Haze removal -- Dark channel prior -- Noise severity -- Atmospheric light -- Particle swarm optimization; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.11.021
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: Image de-hazing from the perspective of noise filtering is proposed. Two shift-scale operations are employed for the construction of two maps No refinement is required due to the pixel-wise noise estimation. The objective of saturation enhancement constrained by hue change is achieved. Analyses reveal the suitability of HRNFP for real-time application. Graphical abstract: Abstract: Digital images captured in outdoor environment are easily polluted by haze, which will degrade the conveyed information. To overcome this problem, a large number of researches have been conducted for image haze removal, among which the approach based on the dark channel prior assumption is in recent years considered as the state-of-the-art. This method is primarily dependent on consolidation of observations. However, in the proposed method, a theoretical perspective is adopted for image, which considers the degraded image as a product contaminated by noise. Two maps are constructed to label the noise severity and atmospheric light. The parameters involved are optimized via Particle Swarm Optimization with a penalty function in terms of hue change. Experimental results were compared with seven available approaches, along with an analysis on algorithm complexity. These analyses verify the effectiveness, efficiency, wide adaptability and theoretical soundness of the proposed approach.