A problem of emphasizing features of a surface roughness by means the Discrete Wavelet Transform

When we are interested to the detection of the roughness features by means of the 3D reconstruction, based on photometric stereo techniques, an important problem is the elimination of the brightness variation due to different light conditions which can alter the response. This paper will concentrate on presenting results of a new method for eliminating this problem. Every pixel of a picture gives only one number: the brightness of the corresponding point on the object, whereas the surface orientation is described by a normal vector that has two degrees of freedom. The level of brightness depends on many factors as well as the homogeneity of reflection properties of the material or its physical continuity and the surface smoothness or roughness. In this work we will show how the application of the Discrete Wavelet Transform (DWT) to the processing of some images, captured on different light conditions, permits to solve the problem of emphasizing roughness features of a metallic surface. Wavelet transforms can model irregular data patterns such as sharp changes, better than the Fourier transforms and standard statistical procedures (e.g., parametric and non-parametric regressions) and provide a multiresolution approximation to the data. Here we propose, also, a non-parametric method, based on the wavelet theory, for the estimation of the threshold level of a gray levels distribution, obtained from the intensity image matrix.