Open AccessMetric of choosing the optimal parameter setting for edge aware filtering
Author(s) -
Yin Hui,
Zhou Fei,
Li Bin
Publication year - 2022
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/ipr2.12360
Subject(s) - sobel operator , metric (unit) , smoothness , enhanced data rates for gsm evolution , mathematics , edge detection , artificial intelligence , computer science , image (mathematics) , filter (signal processing) , algorithm , measure (data warehouse) , computer vision , pattern recognition (psychology) , image processing , data mining , mathematical analysis , operations management , economics
Most of the existing edge aware filters have a number of parameters. The optimal settings of these parameters guarantee the best performance but they depend on the input image. It would be difficult for inexperienced users to empirically get the optimal parameters. This paper proposes a new metric for choosing the optimal parameter settings of edge aware filtering, which is called metric of edge aware filtering (MEAF). MEAF evaluates the quality of filtered images from three aspects: the color distance, the distance of the prominent structure, and smoothness. The colour distance is calculated by rooted mean square error. The distance of the prominent structure is calculated by the proposed SSIM map masked by Sobel edges (MASKED‐SSIM). In MASKED‐SSIM, Sobel detector is used to detect the prominent structure from the input image and the calculation of structure distance is constrained on the prominent structures. Number of gradients and relative total variation are further defined to measure the smoothness of the filtered image. MEAF is an objective metric, which is specially designed for choosing the optimal parameters of edge aware filtering and can be used universally for arbitrary input image. Experiments on 12 state‐of‐the‐art edge aware filters show the effectiveness of MEAF.