Combined window filtering and its applications

Abstract

We present a new local window based image processing framework, which is particularly effective on edge-preserving and texture-removing. This seemingly contradictive effect is achieved by combining the traditional full window filtering strategy (FWF), which is good at removing noise, and the recently proposed side window filtering (SWF) strategy, which is good at preserving edges, so the new framework is called combined window filtering (CWF). By using window inherent variation method, we can easily distinguish the edges of structures from the texture. For the pixels on edges, SWF is used to preserve them and for the pixels on texture, FWF with multiple scales is used to remove them. This technique is surprisingly simple yet very effective in practice. We show that many traditional linear and nonlinear filters can be easily implemented under CWF framework. Extensive analysis and experiments show that implementing the CWF principle can significantly improve their edge-preserving and texture-removing capabilities and achieve state of the art performances in applications.