Guided Image Filtering: A Survey and Evaluation Study

In the past decade, there has been an increasing success of guided image filtering (GIF). Leveraging the guidance image as a prior and transferring the structural details to the target image, GIF has demonstrated its ability in faithfully preserving image edges while maintaining low computational complexity. Additionally, GIF exhibits good capability in extracting and characterizing images from various domains. Researchers have proposed large numbers of GIF-like variants. Nevertheless, limited effort has been devoted to a systematic review and evaluation of these methods. To fill this gap, this paper provides a comprehensive survey of existing GIF-like methods, including model- and deep learning-based approaches. Moreover, extensive experiments are conducted to compare the performance of 18 representative methods. Analysis of the qualitative and quantitative results reveals several observations concerning the current state of this area.