scispace - formally typeset
Search or ask a question
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
More filters
Patent
29 Dec 1986
TL;DR: In this article, a method for sensing sampled colored image data and then interpolating the sampled image data to provide image data in each color sampled for each point or pixel at which the subject is sensed from which an image of the subject may be constructed having reduced color artifacts and fringing while reducing the blurring.
Abstract: Apparatus and method for sensing sampled colored image data and thereafter interpolating the sampled colored image data to provide image data in each color sampled for each point or pixel at which the subject is sensed from which an image of the subject may be constructed having reduced color artifacts and fringing while reducing the blurring to the image that would otherwise be required to correct for such artifacts and fringing.

204 citations

Journal ArticleDOI
TL;DR: A novel and computationally efficient, non-linear signal processing technique for reducing background noise to reveal small biological signals is described, specifically designed for revealing fast transient signals dominated by noise, such as single-channel or post-synaptic currents.

203 citations

Journal ArticleDOI
TL;DR: Four types of noise (Gaussian noise, Salt & Pepper noise, Speckle noise and Poisson noise) are used and image de-noising performed for different noise by Mean filter, Median filter and Wiener filter .
Abstract: Image processing is basically the use of computer algorithms to perform image processing on digital images. Digital image processing is a part of digital signal processing. Digital image processing has many significant advantages over analog image processing. Image processing allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing of images. Wavelet transforms have become a very powerful tool for de-noising an image. One of the most popular methods is wiener filter. In this work four types of noise (Gaussian noise , Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by Mean filter, Median filter and Wiener filter . Further results have been compared for all noises.

203 citations

Journal ArticleDOI
TL;DR: A new fast dehazing method from single image based on filtering that is fast with linear complexity in the number of pixels of the input image and can be further accelerated using GPU, which makes this method applicable for real-time requirement.
Abstract: In this paper, we propose a new fast dehazing method from single image based on filtering. The basic idea is to compute an accurate atmosphere veil that is not only smoother, but also respect with depth information of the underlying image. We firstly obtain an initial atmosphere scattering light through median filtering, then refine it by guided joint bilateral filtering to generate a new atmosphere veil which removes the abundant texture information and recovers the depth edge information. Finally, we solve the scene radiance using the atmosphere attenuation model. Compared with exiting state of the art dehazing methods, our method could get a better dehazing effect at distant scene and places where depth changes abruptly. Our method is fast with linear complexity in the number of pixels of the input image; furthermore, as our method can be performed in parallel, thus it can be further accelerated using GPU, which makes our method applicable for real-time requirement.

202 citations

Journal ArticleDOI
01 Dec 1976
TL;DR: Two procedures are developed to adapt continuously the finite impulse response of a two-dimensional, noncausal, linear digital filter to remove random noise from gray tone images without significantly sacrificing the subjective resolution.
Abstract: The problem of removing random noise from gray tone images without significantly sacrificing the subjective resolution is considered. Based on a subjective visibility function, which gives the relationship between the visibility of a unit noise and a measure of local spatial detail (spatial masking), two procedures are developed to adapt continuously the finite impulse response of a two-dimensional, noncausal, linear digital filter. At sharp transitions in the image intensity, the filter operator is strongly peaked to preserve the resolution, whereas in flat areas it is flat to effectively average out the random noise. The first procedure (S-filter) is computationally more efficient, but does not perform as well as the second method (SD-filter) which requires solution of a new optimization problem at every picture element. Results of several simulations are presented to demonstrate the feasibility of our approach. Extensions are pointed out to incorporate different adaptation procedures and psychovisual criteria other than the type of spatial masking used here.

201 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
92% related
Image processing
229.9K papers, 3.5M citations
91% related
Convolutional neural network
74.7K papers, 2M citations
87% related
Artificial neural network
207K papers, 4.5M citations
86% related
Deep learning
79.8K papers, 2.1M citations
85% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202372
2022186
2021276
2020387
2019478
2018538