On the properties of an image smoothing algorithm for colored images based on gradient analysis

In this paper properties and possible applications of an image smoothing algorithm are explored. The algorithm allows us to remove small textures and to preserve main structures on the image. Algorithm is based on the analysis of two gradient components. These components are the length and the angle of gradient vector. Theory that underlies the algorithm is based on distinction between two types of boundaries, which differ in behavior of gradient vectors. We suppose that closeness of gradient angles in given neighborhood means that points belong to the same boundary. This in turn means that they should have bigger weights. We also take into consideration inverted gradient length as the factor for weight computation. Our goal is to focus on the results of applying the algorithm as a preprocessing step for the tasks like edge detection. This method shows interesting results as a preprocessing step for edge detection tasks. It smooths insignificant details, from which we do not need edges to be shown at the image of the edges. We also explore interesting properties of using algorithm for several iterations and its behavior on noise reduction task.