Volume Upscaling Using Local Self-Examples for High Quality Volume Visualization

Volume up scaling enlarges the size of a volume to make feature analysis more accurate and efficient. Linear interpolation, widely used in volume up scaling, result in jagged artifacts around features and losses of high-frequency components. Based on the example-based up scaling framework, this paper presents a new high-quality volume up scaling technique, predicting the high-frequency components by searching for the best matched patch in the input volume. As each slice can be taken as an image, the existing image up scaling technique based on the local self-similarity assumption can be directly applied to achieve slice up scaling. We further validate that the local self-similarity assumption is still valid for 3D volumes, and we extend this technique to 3D volume up scaling, i.e., isotropic volume up scaling. We compare our volume up scaling technique with traditional linear and cubic spline interpolations, and demonstrate that our method can generate a higher quality volume with better shape and details preserved. The proposed volume up scaling technique is well suitable for legacy low-resolution volumes to improve their visual qualities in visualization and analysis.