Open AccessWindow‐aware guided image filtering via local entropy
Author(s) -
Liu Chong,
Yang Cui,
Wang Jun
Publication year - 2021
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/ipr2.12117
Subject(s) - bilateral filter , smoothing , artificial intelligence , computer science , edge preserving smoothing , entropy (arrow of time) , computer vision , filter (signal processing) , noise reduction , non local means , median filter , image processing , pattern recognition (psychology) , image (mathematics) , physics , quantum mechanics
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.