Dr. Hui Wang Profile

Dr. Hui Wang

at Institute of Optics and Electronics CAS

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Publications (2)

Proceedings Article | 27 September 2016 Paper

A fast algorithm for image defogging

Xingyu Wang, Shuai Guo, Hui Wang, Haibing Su

Proceedings Volume 9684, 968427 (2016) https://doi.org/10.1117/12.2243461

KEYWORDS: Detection and tracking algorithms, RGB color model, Fiber optic gyroscopes, Image enhancement, Image processing, Distortion, Image restoration, Visibility through fog, Visibility, Image compression

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Proceedings Article | 5 August 2009 Paper

An approach to integrate the human vision psychology and perception knowledge into image enhancement

Hui Wang, Xifeng Huang, Jiang Ping

Proceedings Volume 7383, 73830A (2009) https://doi.org/10.1117/12.834956

KEYWORDS: Image enhancement, Human vision and color perception, Psychology, Image quality, Video, Image processing, Stars, Enhanced vision, Infrared imaging, Detection and tracking algorithms

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Image enhancement is very important image preprocessing technology especially when the image is captured in the poor imaging condition or dealing with the high bits image. The benefactor of image enhancement either may be a human observer or a computer vision process performing some kind of higher-level image analysis, such as target detection or scene understanding. One of the main objects of the image enhancement is getting a high dynamic range image and a high contrast degree image for human perception or interpretation. So, it is very necessary to integrate either empirical or statistical human vision psychology and perception knowledge into image enhancement. The human vision psychology and perception claims that humans' perception and response to the intensity fluctuation δu of visual signals are weighted by the background stimulus u, instead of being plainly uniform. There are three main laws: Weber's law, Weber- Fechner's law and Stevens's Law that describe this phenomenon in the psychology and psychophysics. This paper will integrate these three laws of the human vision psychology and perception into a very popular image enhancement algorithm named Adaptive Plateau Equalization (APE). The experiments were done on the high bits star image captured in night scene and the infrared-red image both the static image and the video stream. For the jitter problem in the video stream, this algorithm reduces this problem using the difference between the current frame's plateau value and the previous frame's plateau value to correct the current frame's plateau value. Considering the random noise impacts, the pixel value mapping process is not only depending on the current pixel but the pixels in the window surround the current pixel. The window size is usually 3×3. The process results of this improved algorithms is evaluated by the entropy analysis and visual perception analysis. The experiments' result showed the improved APE algorithms improved the quality of the image, the target and the surrounding assistant targets could be identified easily, and the noise was not amplified much. For the low quality image, these improved algorithms augment the information entropy and improve the image and the video stream aesthetic quality, while for the high quality image they will not debase the quality of the image.

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