Guided image filtering-conventional to deep models: A review and evaluation study

Abstract

In the past decade, guided image filtering (GIF) has emerged as a successful edge-preserving smoothing technique designed to remove noise while retaining important edges and structures in images. By leveraging a well-aligned guidance image as the prior, GIF has become a valuable tool in various visual applications, offering a balance between edge preservation and computational efficiency. Despite the significant advancements and the development of numerous GIF variants, there has been limited effort to systematically review and evaluate the diverse methods within this research community. To address this gap, this paper offers a comprehensive survey of existing GIF variants, covering both conventional and deep learning-based models. Specifically, we begin by introducing the basic formulation of GIF and its fast implementations. Next, we categorize the GIF follow-up methods into three main categories: local methods, global methods and deep learning-based methods. Within each category, we provide a new sub-taxonomy to better illustrate the motivations behind their design, as well as their contributions and limitations. We then conduct experiments to compare the performance of representative methods, with an analysis of qualitative and quantitative results that reveals several insights into the current state of this research area. Finally, we discuss unresolved issues in the field of GIF and highlight some open problems for further research.