Real-Time Continuous Image Processing

In this work, we propose a framework that performs a number of popular image-processing operations in the continuous domain. This is in contrast to the standard practice of defining them as operations over discrete sequences of sampled values. The guiding principle is that, in order to prevent aliasing, nonlinear image-processing operations should ideally be performed prior to prefiltering and sampling. This is of course impractical, as we may not have access to the continuous input. Even so, we show that it is best to apply image-processing operations over the continuous reconstruction of the input. This transformed continuous representation is then prefiltered and sampled to produce the output. The use of high-quality reconstruction strategies brings this alternative much closer to the ideal than directly operating over discrete values. We illustrate the advantages of our framework with several popular effects. In each case, we demonstrate the quality difference between continuous image-processing, their discrete counterparts and previous anti-aliasing alternatives. Finally, our GPU implementation shows that current graphics hardware has enough computational power to perform continuous image processing in real-time.