In the real world, the color appearances of objects are generally not consistent. It depends principally on two factors: illumination spectral power distribution (illumination color) and intrinsic surface properties. Consequently, to obtain objects' consistent color descriptors, we have to deal with those two factors. The former is commonly referred to as color constancy: a capability to estimate and discount the illumination color, while the latter is identical to the problem of recovering body color from highlights. This recovery is crucial because highlights emitted from opaque inhomogeneous objects can cause the surface colors to be inconsistent with regard to the change of viewing and illuminant directions. We base our color constancy methods on analyzing highlights or specularities emitted from opaque inhomogeneous objects. We have successfully derived a linear correlation between image chromaticity and illumination chromaticity. This linear correlation is clearly described in inverse-intensity chromaticity space, a novel two-dimensional space we introduce. Through this space, we become able to effectively estimate illumination chromaticity (illumination color)from both uniformly colored surfaces and highly textured surfaces in a single integrated framework, thereby making our method significantly advanced over the existing methods. Meanwhile, for separating reflection components, we propose an approach that is based on an iterative framework and a specular-free image. The specular-free image is an image that is free from specularities yet has different body color from the input image. In general, the approach relies principally on image intensity and color. All methods of color constancy and reflection-components separation proposed in this paper are analyzed based on physical phenomena of the real world, making the estimation more accurate, and have strong basics of analysis. In addition, all methods require only a single input image. This is not only practical, but also challenging in term of complexity.