Two comprehensive evaluation metrics, image perceptual quality based on target detectability （PQTD） and perceptual quality based on scene understanding （PQSU）, are proposed to measure image quality for visible and infrared color fusion images of typical scenes. A psychophysical experiment is performed to explore the relationship between conventional quality attributes and the proposed evaluation metrics. The prediction models for PQTD and PQSU are derived by multiple linear regression statistical analyses. Results show that the variation of PQTD can be predicted by sharpness and perceptual contrast between the target and background, and that color harmony and sharpness can predict PQSU. The proposed evaluation metrics and their prediction models provide a foundation for further developing objective quality evaluation of color fusion images.
An infrared image detail enhancement method based on local adaptive gamma correction （LAGC） is proposed. The local adaptive gamma values are designed based on the Weber curve to enhance effectively the image details. Subsequently, the active grayscale range of the image processed by LAGC is further extended by using our proposed histogram statistical stretching. The experimental results show that the proposed algorithm could considerably increase the image details and improve the contrast of the entire image. Thus, it has significant potential for practical applications.
