Edge-Preserving Texture Suppression Filter Based on Joint Filtering Schemes

Abstract

Obtaining a texture-smoothing and edge-preserving filtered output is significant to image decomposition. Although the edge and the texture have salient difference in human vision, automatically distinguishing them is a difficult task, for they have similar intensity difference or gradient response. The state-of-the-art edge-preserving smoothing (EPS) based decomposition approaches are hard to obtain a satisfactory result. We propose a novel edge-preserving texture suppression filter, exploiting the joint bilateral filter as a bridge to achieve the purpose of both properties of texture-smoothing and edge-preserving. We develop the iterative asymmetric sampling and the local linear model to produce the degenerative image to suppress the texture, and apply the edge correction operator to achieve edge-preserving. An efficient accelerating implementation is introduced to improve the performance of filtering response. The experiments demonstrate that our filter produces satisfactory outputs with both properties of texture-smoothing and edge-preserving, while compared with the results of other popular EPS approaches in signal, visual and time analysis. Finally, we extend our filter to a variety of image processing applications.