A class of adaptive directional image smoothing filters

Cataloged from PDF version of article.The gray level distribution around a pixel of an image usually tends to be more coherent in some directions compared to other directions. The idea of adaptive directional filtering is to estimate the direction of higher coherence around each pixel location and then to employ a window which approximates aline segment in that direction. Hence, the details of the image may be preserved while maintaining a satisfactory level of noise suppression performance. In this paper we describe a class of adaptive directional image smoothing filters based on generalized Gaussian distributions. We propose a measure of spread for the pixel values based on the maximum likelihood estimate of a scale parameter involved in the generalized Gaussian distribution. Several experimental results indicate a significant improvement compared to some standard filters. Copyright (C) 1996 Pattern Recognition Society

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation

Observations on adaptive vector filters for noise reduction in color images

In a series of papers, Plataniotis et al. proposed a number of filters for noise reduction in color images where the noise type is unknown. In this letter, those filters with a unified notation are summarized, and it is shown that they are essentially variants of the same filtering procedure. It is also shown that the class of adaptive vector filters can be considered as interpolants between the arithmetic mean filter and the vector median filter. Results are presented of numerical computations with the filters on test images corrupted with noise. It is found that the adaptive vector filters perform well with general applicability

3-D Center-Weighted Vector Directional Filters for Noisy Color Sequences

This paper focuses on a noise filtering in color image sequences, where a new class of center-weighted vector directional filters is provided. According to high dimensionality of color image sequences, where besides the spatial frequencies in the frames it is necessary to consider the temporal correlation of an image sequence and the correlation between color channels too, the processing of color image sequences represents very important and interesting problem. Clearly, the color image sequences represent three-dimensional (3-D) vector-valued image signals and thus, the 3-D vector filters provide optimal approach, only. Novelty of this paper lies in the impulse noise suppression by a new class of center-weighted vector directional filters, where the influence of the filter parameter to filter performance is analyzed. The interesting behavior of a new filter class is illustrated by a number of experimental results and comparisons with the well-known filtering algorithms for color image sequences