BACKGROUND CT perfusion images have a high contrast ratio between voxels representing different anatomy, such as tissue or vessels, which makes image segmentation of tissue and vascular regions relatively easy. However, grey and white matter tissue regions have relatively low values and can suffer from poor signal to noise ratios. While smoothing can improve the image quality of the tissue regi...

Smoothing along structures apparent in seismic images can enhance these structural features while preserving important discontinuities such as faults or channels. Filters appropriate for such smoothing must seamlessly adapt to variations in the orientation and coherence of image features. I describe an implementation of smoothing filters that does this and is both computationally efficient and ...

405 Abstract— Image processing operations like smoothing and edge detection, and many more are very widely used in areas like Computer Vision. We classify the image processing domain as seven branches-image acquirement and output, image coding and compression, image enhancement and restoration, image transformation, image segmentation, image description, and image recognition and description. W...

Image Enhancement is the art of examining images for identifying objects and judging their significance. The aim of image enhancement is to improve the interpretability or perception of information in images for human viewers, or to provide `better' input for other automated image processing techniques. Smoothing is often used to reduce noise within an image or to produce a less pixilated image...

annihilation or reduction of each kind of noise blended in correct data signals is a field that has attracted many researchers. it is a fact that fuzzy theory presents full capability in this field. fuzzy filters are often strong in smoothing corrupted signals, whereas they have simple structures. in this paper, a new powerful yet simple fuzzy procedure is introduced for sharpness reduction in ...

Smoothing algorithms allow to reduce artifacts from mesh generation, but often degrade accuracy. Thus, we present a method that identifies staircase artifacts which result from image inhomogeneities and binary segmentation in medical image data for subsequent removal by adaptive mesh smoothing. This paper makes the following specific contributions: caps, which are flat regions, resulting from s...

It has been shown in literature that adaptive regularized image restoration is superior to non-adaptive one. However, the adaptivity introduced in most proposed iterative algorithms is based only on the application of space-variant smoothing operator. It is found that these adaptive algorithms su er from insu cient smoothing of the at image regions. In this paper, an adaptive iterative image re...

In this paper we use multiscale characteristics of wavelet decompositions and their relationship to smoothness spaces such as Besov spaces to derive a framework for smoothing and sharpening of signals and images. As a result, we derive a multiscale generalization of traditional techniques, such as unsharp masking, while using the smoothness parameter α in the Besov space Bα q (L p) to provide a...