SUPPRESSION OF INHOMOGENEITIES IN IMAGES OF TEXTURED SURFACES

Automated visual inspection tasks are frequently concerned with the ex amination of homogeneously textured surfaces such as fabrics, wallpape rs, machined surfaces, and floorcoverings. Often, the images taken fro m such surfaces are degraded by an intensity inhomogeneity due to the image acquisition process. This inhomogeneity is considered to be an i rrelevant and disturbing signal component, which should be suppressed to enhance the desired texture component and to ease a subsequent text ure analysis. We show that, especially for textured surfaces, it is no t 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 pres ent image processing methods for the homogenization of first, second, and infinite degree. For the homogenization of second degree, we propo se a computationally efficient frequency domain signal processing meth od with high homogenization performance and low nonlinear distortion. Furthermore, we suggest a high-performance homogenization of the infin ite-degree technique that equates the local histograms to a global his togram, which is adapted to the given image data. We compare the propo sed homogenization methods visually and quantitatively with the well-k nown homomorphic filtering technique, which assumes a pure multiplicat ive inhomogeneity. We demonstrate that our methods achieve much better results for synthetic as well as for realistic images of textured sur faces. (C) 1997 Society of Photo-Optical Instrumentation Engineers.