Smooth and iteratively Restore: A simple and fast edge-preserving smoothing model
This work addresses the need for efficient and effective edge-preserving smoothing in image processing, particularly for removing noise and unimportant details without blurring edges, with incremental improvements over existing filters.
The authors tackled the problem of edge-preserving smoothing in image processing by proposing a two-step framework that first blurs the image and then restores important edges, which effectively removes unwanted small structures while maintaining sharp edges. The method delivers very good results compared to state-of-the-art filters and is fast, making it suitable for real-time applications.
In image processing, it can be a useful pre-processing step to smooth away small structures, such as noise or unimportant details, while retaining the overall structure of the image by keeping edges, which separate objects, sharp. Typically this edge-preserving smoothing process is achieved using edge-aware filters. However such filters may preserve unwanted small structures as well if they contain edges. In this work we present a novel framework for edge-preserving smoothing which separates the process into two different steps: First the image is smoothed using a blurring filter and in the second step the important edges are restored using a guided edge-aware filter. The presented method proves to deliver very good results, compared to state-of-the-art edge-preserving smoothing filters, especially at removing unwanted small structures. Furthermore it is very versatile and can easily be adapted to different fields of applications while at the same time being very fast to compute and therefore well-suited for real time applications.