This paper presents a new, unified technique to perform general edge-sensitive editing operations on n-dimensional images and videos efficiently. The contribution of the paper is two-fold. First, a novel unified framework is introduced which addresses several, edge-aware editing operations efficiently. Diverse editing tasks such as: segmentation, de-noising, non-photorealistic rendering, colorization and panoramic stitching are all dealt with fundamentally the same, fast algorithm. Second, a new, geodesic, symmetric filter (GSF) is presented which imposes contrast-sensitive spatial smoothness onto the output data.The effect of the filter is controlled by two intuitive, geometric parameters.

In contrast to existing techniques, the GSF filter is applied to real-valued pixel likelihoods and thus it can be used for both interactive and automatic editing tasks. Complex object topologies are dealt with effortlessly. Finally, the algorithm’s parallelism enables us to exploit modern multi-core CPU architectures as well as powerful new GPUs, thus providing great flexibility of implementation and deployment. Our technique operates on both images and videos, and generalizes naturally to n-dimensional data.

The proposed algorithm is validated via rigorous quantitative and qualitative comparisons with existing, state of the art approaches. Numerous results on a variety of image and video editing tasks further demonstrate the effectiveness of our method.