Procedural Image Processing for Visualization

We present a novel Procedural Image Processing (PIP) method and demonstrate its applications in visualization. PIP modulates the sampling positions of a conventional image processing kernel (e.g. edge detection filter) through a procedural perturbation function. When properly designed, PIP can produce a variety of styles for edge depiction, varying on width, solidity, and pattern, etc. In addition to producing artistic stylization, in this paper we demonstrate that PIP can be employed to achieve various visualization tasks, such as contour enhancement, focus+context visualization, importance driven visualization and uncertainty visualization. PIP produces unique effects that often either cannot be easily achieved through conventional filters or would require multiple pass filtering. PIP perturbation functions are either defined by analytical expressions or encoded in pre-generated images. We leverage the programmable fragment shader of the current graphics hardware for achieving the operations in real-time.