Proc. SPIE. 2657, Human Vision and Electronic Imaging
KEYWORDS: Databases, Image processing, Color difference, Color image processing, Image enhancement, Human vision and color perception, Electronic filtering, RGB color model, Digital color imaging, Data fusion
A tenet of a new class of color image enhancement algorithms involves the observation that the saturation component of color images often contains what appears to be valid image structure depicting the underlying scene. In this work we present the findings of a study of the structural correspondence between the saturation and luminance components of a large database of color images. Various statistical relationships are identified. The correspondence of edges at different scales in the sense of Marr's theory of vision is also observed. Several new color image enhancement algorithms which exploit these unique characteristics are described.
The component-wise processing of color image data in performed in a variety of applications. These operations are typically carried out using Lookup Table (LUT) based processing techniques, making them well suited for digital implementation. A general exposition of this type of processing is provided, indicating it's remarkable utility along with some of the practical issues that can arise. These motivate a call for the use of constraints in the types of operators that are used during the construction of LUTs. Several particularly useful classes of constrained operators are identified. These lead to an object-oriented approach generalized to operated in a variety of color spaces. The power of this type of framework is then demonstrated via several novel applications in the HSL color space.