Window‐aware guided image filtering via local entropy

Abstract

Guided image filtering is one of the widely used techniques in computer vision. However, it commonly leads to over-smoothed edges and a distorted appearance when tackling intricate texture patterns and complex noise. In this paper, a window-aware image filtering framework based on the bilateral filter guided by the local entropy is presented. The key idea of the authors' proposed approach is to design a novel guidance input and a non-box filtering window. Specifically, using the Gaussian spatial kernel and the local entropy, a GEF that can maintain image feature details and yield a robust guidance input for BF is constructed. Meanwhile, based on an intensity-similar strategy, the local non-box filtering window is designed for the further preservation of edge structures. The authors' approach not only inherits the advantages of bilateral filter i.e. simplicity, parallelisation and easiness of programming, but also is more powerful than bilateral filter and its variants. In addition, the guided entropy filter and the non-box window can also be transplanted to other local filters and can effectively improve the filtering effects. The qualitative and quantitative experimental results demonstrate that the authors' approach has good performance in image denoising, texture (or background) smoothing, edge extraction and other applications in image processing.