Propagated image filtering

Abstract

We propose the propagation filter as a novel image filtering operator, with the goal of smoothing over neighboring image pixels while preserving image context like edges or textural regions. In particular, our filter does not to utilize explicit spatial kernel functions as bilateral and guided filters do. We will show that our propagation filter can be viewed as a robust estimator, which minimizes the expected difference between the filtered and desirable image outputs. We will also relate propagation filtering to belief propagation, and suggest techniques if further speedup of the filtering process is necessary. In our experiments, we apply our propagation filter to a variety of applications such as image denoising, smoothing, fusion, and high-dynamic-range (HDR) compression. We will show that improved performance over existing image filters can be achieved.