Robust propagated filtering with applications to image texture filtering and beyond
Extracting meaningful structures from an image is an important task and benefits a wide range of image application tasks. However, it is typically very challenging to distinguish between noisy or textural patterns from image structures, especially when such patterns do not exhibit regularity (e.g., irregular textural patterns or those with varying scales). While existing edge-preserving image filters like bilateral, guided, or propagation filters aim at observing strong image edges, they cannot be easily applied to solve the above texture filtering tasks. In this paper, we propose robust propagated filter, which is an extension to propagation filters while exhibiting excellent ability in eliminating the aforementioned textural patterns when performing filtering. We show in our experimental results that our filter provides promising results on image filtering. Additional experiments on inverse image half toning and detail enhancement further verify the effectiveness of our proposed method.