Co-occurrence Filter

TL;DR

Co-occurrence Filter (CoF) is a boundary-preserving image filter that uses a learned co-occurrence matrix to smooth textured regions while maintaining boundaries, extending the Bilateral Filter's capabilities.

Contribution

The paper introduces CoF, a novel boundary-preserving filter that learns co-occurrence matrices directly from images to improve edge and boundary preservation.

Findings

CoF effectively smooths textured regions.

It preserves boundaries better than traditional filters.

Applicable to color images and various use cases.

Abstract

Co-occurrence Filter (CoF) is a boundary preserving filter. It is based on the Bilateral Filter (BF) but instead of using a Gaussian on the range values to preserve edges it relies on a co-occurrence matrix. Pixel values that co-occur frequently in the image (i.e., inside textured regions) will have a high weight in the co-occurrence matrix. This, in turn, means that such pixel pairs will be averaged and hence smoothed, regardless of their intensity differences. On the other hand, pixel values that rarely co-occur (i.e., across texture boundaries) will have a low weight in the co-occurrence matrix. As a result, they will not be averaged and the boundary between them will be preserved. The CoF therefore extends the BF to deal with boundaries, not just edges. It learns co-occurrences directly from the image. We can achieve various filtering results by directing it to learn the co-occurrence matrix from a part of the image, or a different image. We give the definition of the filter, discuss how to use it with color images and show several use cases.