Median filter

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

A new fuzzy logic filter for image enhancement

Abstract: This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.

Realisation of the first sub shot noise wide field microscope

Abstract: In the last years several proof of principle experiments have demonstrated the advantages of quantum technologies respect to classical schemes. The present challenge is to overpass the limits of proof of principle demonstrations to approach real applications. This letter presents such an achievement in the field of quantum enhanced imaging. In particular, we describe the realization of a sub-shot noise wide field microscope based on spatially multi-mode non-classical photon number correlations in twin beams. The microscope produces real time images of 8000 pixels at full resolution, for (500micrometers)2 field-of-view, with noise reduced to the 80% of the shot noise level (for each pixel), suitable for absorption imaging of complex structures. By fast post-elaboration, specifically applying a quantum enhanced median filter, the noise can be further reduced (less than 30% of the shot noise level) by setting a trade-off with the resolution, demonstrating the best sensitivity per incident photon ever achieved in absorption microscopy.

Method of cascading noise reduction algorithms to avoid speech distortion

Abstract: A method of reducing noise by cascading a plurality of noise reduction algorithms is provided. A sequence of noise reduction algorithms are applied to the noisy signal. The noise reduction algorithms are cascaded together, with the final noise reduction algorithm in the sequence providing the system output signal. The sequence of noise reduction algorithms includes a plurality of noise reduction algorithms that are sufficiently different from each other such that resulting distortions and artifacts are sufficiently different to result in reduced human perception of the artifact and distortion levels in the system output signal.

Image Denoising by Sparse Code Shrinkage

Abstract: Sparse coding is a method for finding a neural network representation of multidimensional data in which each of the components of the representation is rarely ignorantly active at the same time. The representation is closely related to independent component analysis (ICA). In this paper, we introduced the basic principle of ICA and have investigated the capabilities of sparse coding shrinkage in the field of image denoising. We have also performed practical implementation of sparse code shrinkage (SCS) and applied to the image denoising. We have seen that SCS outperforms basic denoising methods such as wiener filtering, median filtering and Independent Component Analysis (ICA) applied to image denoising.

Using local median as the location of the prior distribution in iterative emission tomography image reconstruction

Abstract: Iterative reconstruction algorithms like MLEM (Maximum Likelihood Expectation Maximization) can be regularized using a weighted roughness penalty term according to certain a priori assumptions of the desired image. In the R?RP (Median Root Prior) algorithm the penalty is set according to the deviance of a pixel from the local median. This allows both noise reduction and edge preservation. The prior distribution is Gaussian located around the median of a neighborhood of the pixel. Non-monotonic details smaller than a given limit are considered as noise and are penalized. Thus, MRP implicitly contains the general description of the characteristics of the desired emission image, and good localization of tissue boundaries is achieved without anatomical data. In contrast to the MLEM method, the number of iterations needs not be restricted and unlike many other Bayesian methods MRP has only one parameter. The penalty term can be applied to various iterative reconstruction algorithms. The assumption that the true pixel value is close to the local median applies to any emission images, including the 3D acquisition and images reconstructed from parametric sinograms.