Image quality improvement using local adaptive neighborhood-based dark channel prior

Toshiki Onoyama; Huimin Lu; Afzal Ahmed Soomro; Ainul Akmar Mokhtar; Tohru Kamiya; Seiichi Serikawa

doi:10.1117/12.2603771

28 October 2021 Image quality improvement using local adaptive neighborhood-based dark channel prior

Toshiki Onoyama, Huimin Lu, Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Tohru Kamiya, Seiichi Serikawa

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118840M (2021) https://doi.org/10.1117/12.2603771
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

In-vehicle cameras and surveillance cameras are used in many situations in our daily lives. Visibility degradation in foggy environments is caused by the scattering of reflected light from real objects by minute water droplets or fog in the medium through which light passes. The degree of degradation depends on the density of suspended microparticles existing between the observed object and the observation point in the medium. In general, the farther the object is from the camera, the more it is affected by the fog. The purpose of image de-fogging is to improve the clarity of an object by removing the effects of fog in the image.

Citation Download Citation

Toshiki Onoyama, Huimin Lu, Afzal Ahmed Soomro, Ainul Akmar Mokhtar, Tohru Kamiya, and Seiichi Serikawa "Image quality improvement using local adaptive neighborhood-based dark channel prior", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118840M (28 October 2021); https://doi.org/10.1117/12.2603771

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