CAS-SFCM: Content-Aware Image Smoothing Based on Fuzzy Clustering with Spatial Information

Image smoothing is a low-level image processing task mainly aimed at homogenizing an image, mitigating noise, or improving the visibility of certain image areas. There exist two main strategies for image smoothing. The first strategy is content-unaware image smoothing. This strategy replicates identical smoothing behavior at every region in the image, hence ignoring any local or semi-local properties of the image. The second strategy is content-aware image smoothing, which takes into account the local properties of the image in order to adapt the smoothing behavior. Such adaptation to local image conditions is intended to avoid the blurring of relevant structures (such as ridges, edges, and blobs) in the image. While the former strategy was ubiquitous in the early years of image processing, the last 20 years have seen an ever-increasing use of the latter, fueled by a combination of greater computational capability and more refined mathematical models. In this work, we propose a novel content-aware image smoothing method based on soft (fuzzy) clustering. Our proposal capitalizes on the strengths of soft clustering to produce content-aware smoothing and allows for the direct configuration of the most relevant parameters for the task: the number of distinctive regions in the image and the relative relevance of spatial and tonal information in the smoothing. The proposed method is put to the test on both artificial and real-world images, combining both qualitative and quantitative analyses. We also propose the use of a local homogeneity measure for the quantitative analysis of image smoothing results. We show that the proposed method is not sensitive to centroid initialization and can be used for both artificial and real-world images.