Automated visual inspection tasks are frequently concerned with the examination of homogeneously textured surfaces such as fabrics, wallpapers, machined surfaces, and floorcoverings. Often, the images taken from such surfaces are degraded by an intensity inhomogeneity due to the image acquisition process. This inhomogeneity is considered to be an irrelevant and disturbing signal component, which should be suppressed to enhance the desired texture component and to ease a subsequent texture analysis. We show that, especially for textured surfaces, it is not always reasonable to assume a pure multiplicative composition of the texture signal and a disturbing inhomogeneity. We introduce a notion of homogeneity of n’th degree based on first-order statistics and present image processing methods for the homogenization of first, second, and infinite degree. For the homogenization of second degree, we propose a computationally efficient frequency domain signal processing method with high homogenization performance and low nonlinear distortion. Furthermore, we suggest a high-performance homogenization of the infinite-degree technique that equates the local histograms to a global histogram, which is adapted to the given image data. We compare the proposed homogenization methods visually and quantitatively with the well-known homomorphic filtering technique, which assumes a pure multiplicative inhomogeneity. We demonstrate that our methods achieve much better results for synthetic as well as for realistic images of textured surfaces.