A mixed noise image filtering method using weighted-linking PCNNs

Authors:
Luping Ji;Zhang Yi
Affiliations:
Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China;Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, PR China
Venue:
Neurocomputing
Year:
2008

Citing 22
Cited 5

Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detail-preserving median based filters in image processing

Pattern Recognition Letters
A neural network adaptive filter for the removal of impulse noise in digital images

Neural Networks
A deconvolution technique using optimal Wiener filtering and regularization

Signal Processing
Geometry-Driven Diffusion in Computer Vision

Geometry-Driven Diffusion in Computer Vision
Image Processing Using Pulse-Coupled Neural Networks

Image Processing Using Pulse-Coupled Neural Networks
Wavelet-domain iterative center weighted median filter for image denoising

Signal Processing
Adaptive vector median filtering

Pattern Recognition Letters
Letters: A class of binary images thinning using two PCNNs

Neurocomputing
Feature linking via synchronization among distributed assemblies: Simulations of results from cat visual cortex

Neural Computation
A new efficient approach for the removal of impulse noise from highly corrupted images

IEEE Transactions on Image Processing
Image recovery using the anisotropic diffusion equation

IEEE Transactions on Image Processing
Forward-and-backward diffusion processes for adaptive image enhancement and denoising

IEEE Transactions on Image Processing
Image denoising using scale mixtures of Gaussians in the wavelet domain

IEEE Transactions on Image Processing
A universal noise removal algorithm with an impulse detector

IEEE Transactions on Image Processing
A switching median filter with boundary discriminative noise detection for extremely corrupted images

IEEE Transactions on Image Processing
An adaptive recursive 2-D filter for removal of Gaussian noise in images

IEEE Transactions on Image Processing
PCNN models and applications

IEEE Transactions on Neural Networks
Perfect image segmentation using pulse coupled neural networks

IEEE Transactions on Neural Networks
Inherent features of wavelets and pulse coupled networks

IEEE Transactions on Neural Networks
Object detection using pulse coupled neural networks

IEEE Transactions on Neural Networks
Image shadow removal using pulse coupled neural network

IEEE Transactions on Neural Networks

Restoration of images corrupted by Gaussian and uniform impulsive noise

Pattern Recognition
Noise and speckle reduction in doppler blood flow spectrograms using an adaptive pulse-coupled neural network

EURASIP Journal on Advances in Signal Processing - Special issue on applications of time-frequency signal processing in wireless communications and bioengineering
Restoration of images corrupted by mixed Gaussian-impulse noise via l1-l0 minimization

Pattern Recognition
Review: Pulse coupled neural networks and its applications

Expert Systems with Applications: An International Journal
A novel diffusion system for impulse noise removal based on a robust diffusion tensor

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Image is often degraded by more than one type of noise. In order to design an efficient filter to remove mixed noise from image, this paper proposes a weighted-linking pulse coupled neural network (PCNN) model so as to construct a two-channel parallel noise filter using four PCNNs of this model. This filter detects noise using the pulses generated by neurons, and iteratively removes noise by the pixel signal variation of pulse neurons. The filtering parameters and the iteration stopping conditions are discussed. Experiments show that the proposed PCNN-based filtering method is fast and effective for removing single impulse noise, additional Gaussian noise, as well as the mixed noise of them.