Structure Preserving Image Smoothing

Structure-oriented bilateral filtering of seismic images

Bilateral filtering is widely used to enhance photographic images, but in most implementations is poorly suited to seismic images. A bilateral filter consists of two (domain and range) filter kernels. By replacing the domain kernel with a smoothing filter that conforms to image structures, we obtain a bilateral filter suitable for seismic image processing. Examples and comparison with conventio...

Structure-oriented bilateral filtering

Bilateral filtering is widely used to enhance photographic images, but in most implementations is poorly suited to seismic images. A bilateral filter consists of two filter kernels. By replacing one of those kernels with a smoothing filter that conforms to image structures, we obtain a bilateral filter suitable for seismic image processing. Examples and comparison with conventional edge-preserv...

Semantic-Aware Image Smoothing

Structure-preserving image smoothing aims to extract semantically meaningful image structure from texture, which is one of the fundamental problems in computer vision and graphics. However, it is still not clear how to define this concept. On the other hand, semantic image labeling has achieved significant progress recently and has been widely used in many computer vision tasks. In this paper, ...

Smooth and iteratively Restore: A simple and fast edge-preserving smoothing model

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 con...

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