The roughness of rock joints is a relevant factor considerably influencing the shear strength of rock masses, which is critical for the mechanical stability of large civil engineering structures. One of the geotechnical methods for determining the roughness coefficients of rock joints is on the basis of the visual comparison of the joint surfaces with the standard two-dimensional profiles whose roughness coefficients are known. Visual comparison is rather a subjective method, but it may be improved by a fully computerized image recognition procedure, and the two-dimensional standard profiles may be replaced by three-dimensional database profiles. To attain a higher stage of objectivity, a new alternative method was proposed on the basis of the application of Fourier’s formalism to three-dimensional surface profiles. The image database recognition was facilitated by introducing two parameters that were defined as height and shape indicators of surface reliefs.

Three-dimensional (3-D) reconstruction is an indispensable tool in areas such as biology, chemistry, medicine, material sciences, etc. The sample can be reconstructed using confocal or nonconfocal mode of a microscope. The limitation of the confocal approach is the sample size. Currently used devices work mostly with sample surface area up to 1 cm2. We suggest a three-step method that creates 3-D reconstruction from multifocal images in nonconfocal mode that is qualitatively comparable to the confocal results. Our method, thus, takes advantage of both microscope modes—high-quality results without sample size limitation. The preprocessing step eliminates the additive noise with Linderberg-Lévi theorem. The main focus criterion is based on adjusted Fourier transform. In the final step, we eliminate the defective clusters using the adaptive pixel neighborhood algorithm. We proved the effectiveness of our noise reduction and 3-D reconstruction method by the statistical comparisons; the correlation coefficients average 0.987 for all types of Fourier transforms.