dc.description.abstract | While enhancing the intensity or saturation component for high-quality color image enhancement, keeping the hue component unchanged is important; thus, perceptual color models such as HSI, HSL, and HSV were used. Hue-Saturation-Intensity (HSI) is a public color model, many color applications are commonly based on this model; however, the transformation from HSI model to RGB model usually generates the out-of-gamut problem after modifying intensity and saturation in the HSI model. Moreover, the saturation component is always increased or decreased following the change of intensity component no matter what the attainable saturation range is.
The HSV (Hue-Saturation-Value) color model is popular for color image enhancement and segmentation; but the identical-value plane of the HSV color space is parallel to one of the three ceiling planes of the RGB cube, the area of the plane will be extended as the value is increased. Hence the value histogram is concentrated in the high value portion; the low saturation pixels are highly separated from the high saturation ones after enhancement on the value components, such as histogram equalization and histogram stretching, although their hue and intensity are approximate.
In this paper, we propose accurate formulas for the color transformation between RGB and the proposed HSI color model, called the exact HSI (eHSI) color model, to resolve the out-of-gamut problem directly as well as to automatically adapt the saturation range; that is, the saturation component can be enhanced or reduced according to the attainable maximum saturation range. In experiments, we demonstrate how to sacrifice a little contrast to improve the image saturation based on the proposed eHSI color model. On the other hand, we propose an improved HSV color model iHSV, which preserves the Gaussian distribution characteristic of the intensity histogram in RGB cube; that is, the maximum saturation range is smaller on both ends and larger in the central area of the value axis in the corresponding HSV model. We also demonstrate how the saturation of an image can be improved by sacrificing a little contrast in the improved HSV color model. In practice, we can take a counterbalance between intensity enhancement and saturation enhancement to obtain a better quality image based on the characteristics of images.
Furthermore, partial cloud cover is a serious problem in optical remote sensing images. The problem can be mostly resolved by mosaicking the cloud-free areas of multi-temporal images. We propose multidisciplinary methods to generate cloud-free mosaic images from multi-temporal satellite images in three steps. At first, all original images are enhanced in intensity based on the proposed exact HSI (eHSI) color model. Secondly, an intensity thresholding accompanied with a difference comparison method is used to extract all cloud-cover regions. Then we choose the image with the least thin cloud cover as the base image and divide the image into grid zones. We find the thin-cloud and cloud-shadow zones in the eight neighbors of the thick cloud zones based on the relative locations and elevation angle of the sun. Finally, the cloud and cloud-shadow zones of the base image are replaced by the same-location cloud-free zones on other temporal images; then a pyramid multi-scale fusion method is used to generate cloud-free satellite images. Based on the proposed complete approach, fused images with proper brightness and saturation are produced from source images that may have variant brightness.
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