Color Cast Dependent Image Dehazing via Adaptive Airlight Refinement and Non-Linear Color Balancing

• Published in: IEEE transactions on circuits and systems for video technology Vol. 31; no. 5; pp. 2076 - 2081
• Main Authors: Kanti Dhara, Sobhan, Roy, Mayukh, Sen, Debashis, Kumar Biswas, Prabir
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive optics; airlight refinement; Atmospheric modeling; Balancing; Color; color cast removal; dark channel prior; Distortion; Estimation; Haze; Image color analysis; Image dehazing; Linear transformations; Low visibility; Meteorology; Nonlinear optics; Weather; weighted least squares filtering
• ISSN: 1051-8215; 1558-2205
• DOI: 10.1109/TCSVT.2020.3007850

Hazy images suffer from low visibility since the light gets scattered as it passes through various atmospheric particles. Moreover, such images are prone to color distortion, particularly in real weather conditions like sandstorms. In this letter, an effective dehazing technique is proposed using weighted least squares filtering on dark channel prior and color correction that involves automatic detection of color cast images. We show that the spread of the hue in a hazy image can differentiate a color cast image from a non-cast one. We propose a measure using the same for categorizing hazy images as cast and non-cast ones. Our novel color correction is performed by color balancing using a non-linear transformation followed by a cast-adaptive airlight refinement. Subjective and quantitative evaluations show that our method outperforms the state-of-the-art. It removes cast satisfactorily and reduces haze substantially while maintaining the naturalness of the image. Moreover, it produces visually pleasing images without halo artifacts.