Median filter

About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.

Anisotropic noise

Abstract: Programmable graphics hardware makes it possible to generate procedural noise textures on the fly for interactive rendering. However, filtering and antialiasing procedural noise involves a tradeoff between aliasing artifacts and loss of detail. In this paper we present a technique, targeted at interactive applications, that provides high-quality anisotropic filtering for noise textures. We generate noise tiles directly in the frequency domain by partitioning the frequency domain into oriented subbands. We then compute weighted sums of the subband textures to accurately approximate noise with a desired spectrum. This allows us to achieve high-quality anisotropic filtering. Our approach is based solely on 2D textures, avoiding the memory overhead of techniques based on 3D noise tiles. We devise a technique to compensate for texture distortions to generate uniform noise on arbitrary meshes. We develop a GPU-based implementation of our technique that achieves similar rendering performance as state-of-the-art algorithms for procedural noise. In addition, it provides anisotropic filtering and achieves superior image quality.

Generalized homomorphic and adaptive order statistic filters for the removal of impulsive and signal-dependent noise

Abstract: In this paper, we propose two new nonlinear filters for filtering signal-dependent noise, additive noise, and impulsive noise in image processing. The first filter proposed is an order statistic filter based on a generalized homomorphic transformation. The second is an adaptive order statistic filter with a variable threshold, which changes according to the noise level. Both of these filters perform well for the different kinds of noise encountered in image processing. They suppress signal-dependent noise, additive noise, and impulsive noise better than median filters, \alpha -trimmed mean filters, general nonlinear mean filters, modified trimmed mean filters, and double-window modified trimmed mean filters. They also preserve the edges of an image better than median filters and are simple to implement.

Applications of the median filter to digital radiographic images

Abstract: It is well known that Digital Subtraction Angiography (DSA) images suffer from the influence of noise. Linear filters have been applied to DSA images with some success but are known to introduce image degradation. It is particularly important that edge details be preserved in DSA images since the outline of vessels frequently contains the diagnostically useful information. The non-linear median filter possesses the property of removing spurious noise while preserving edge detail in an image. In the work presented here the difficulties of applying a median filter in two dimensions to the cartesian matrix of a typical image are investigated. The development of a suitable noise-added test image for this purpose is presented.

Nearest-neighbour multichannel filter

Abstract: The authors address the problem of noise attenuation for multichannel data. The proposed filter utilises adaptively determined data dependent coefficients. The special case of colour image processing is studied as an important example of multichannel signal processing.

Improved Zhang-Suen thinning algorithm in binary line drawing applications

Abstract: Zhang-Suen parallel thinning algorithm with the feature of rapidity and practicality ensures the connectivity of the refined curve. However, the refined skeleton cannot be guaranteed in a single pixel wide, and redundancy segments are generated due to acute angles. This paper is based on Zhang-Suen thinning algorithm and first utilizes the median filter for image processing, then refines the skeleton to a single pixel wide and removes the redundancy segments. In this method, the excellent characteristics of the original algorithm can be maintained and problems in the original algorithm can be solved.