Topic
Median filter
About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, a moving differential median filter is used to minimize line-level errors and distortion of high-wavenumber anomalies when processing irregular survey lines, making the method suitable for a wide variety of data sets.
Abstract: We describe a new technique that can be used to level data collected along regular and irregular line patterns with or without tie-line control. The technique incorporates a moving differential median filter to minimize line-level errors, to level survey-line data, and to microlevel data with no tie-line control. This overcomes the problem of standard leveling methods that lose their effectiveness with irregular flight patterns. To validate the method, we use it to level very-low-frequency (VLF) electromagnetic (EM) data from a helicopter survey where flight lines are parallel. Leveling is also performed on a set of vintage aeromagnetic data from the North Sea, gathered from nonparallel flight lines. Results show that the differential median filter leveling technique is superior to the standard leveling method because it results in fewer line errors and less distortion of high-wavenumber anomalies when processing irregular survey lines, making the method suitable for a wide variety of data sets.
44 citations
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TL;DR: The vector processing results in a simpler and more accurate image enhancement algorithm in comparison with scalar processing, and the performance of the vector and scalar estimators is compared.
Abstract: A new approach to design of a recursive image enhancer is introduced when the image is characterized statistically by its mean and correlation function. A vector linear dynamical model is derived to represent the statistics of the processor output when several lines of the picture are processed simultaneously. Based on the vector model, a Kalman filter is designed and utilized to recursively enhance the image. The vector processing results in a simpler and more accurate image enhancement algorithm in comparison with scalar processing. Two examples, one with very low signal-to-noise ratio, are used to illustrate the effectiveness of the procedure. Finally, the performance of the vector and scalar estimators is compared.
44 citations
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01 Mar 1987TL;DR: A straight line extraction method has been employed by which one can obtain the polygonal approximation of the shape of the object by sequential touching and integrating the straight lines so extracted for larger objects.
Abstract: Some of the edge detection methods well known in robotic vision systems have been applied to detect edges in tactile images. Two dimensional median filtering with a 3 × 3 window size has been employed for the removal of noise present in the tactile images with excellent results. The LTS-200 tactile sensor system developed by the LORD Corporation has been used to obtain the tactile images analyzed in this research. The study has been carried out to obtain the contours of objects smaller in size than the LTS-200 sensor (with an active area of 1.131 in. × 0.707 in.) with a single probing operation. For larger objects, a straight line extraction method has been employed by which one can obtain the polygonal approximation of the shape of the object by sequential touching and integrating the straight lines so extracted.
44 citations
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TL;DR: A new method for image segmentation of X-ray microtomography data that is especially adapted for samples where the feature of interest is about the same order of magnitude as the resolution of the data is presented, which combines two filters that are widely used in image processing, Unsharp Mask and Median Filter.
44 citations
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TL;DR: This paper proposed a method to remove a noise that is popular in biomedicine that can be considered as a combination of Gaussian and Poisson noises, based on the total variation of an image intensity (brightness) function.
Abstract: There are many modern devices are used to create digital images. These devices use optical effects to create images. Therefore, the image quality depends on quality of optical sensors. Because of the limits of technology, these sensors cannot reconstruct the images perfectly, and always include some defects. One from these defects is noise. The noise reduces image quality and result of image processing. The image noises can be classified into some types: Gaussian noise, Poisson noise, speckle noise and so on. Depending on particular noises, we have efficient methods to remove them. There is no existing a universal method to remove all noises effectively. In this paper, we proposed a method to remove a noise that is popular in biomedicine. This noise can be considered as a combination of Gaussian and Poisson noises. Our method is based on the total variation of an image intensity (brightness) function.
44 citations