A comprehensive survey on impulse and Gaussian denoising filters for digital images

In this study an approach to impulse noise removal is presented. The introduced algorithm is a switching filter which identifies the noisy pixels and then corrects them by using median filter. In order to identify pixels corrupted by noise an analysis of local intensity extrema is applied. Comprehensive analysis of the algorithm performance [in terms of peak signal-to-noise ratio (PSNR) and Structural SIMilarity (SSIM) index] is presented. Results obtained on wide range of noise corruption (up to 98%) are shown and discussed. Moreover, comparison with well-established methods for impulse noise removal is provided. Presented results reveal that the proposed algorithm outperforms other approaches to impulse noise removal and its performance is close to ideal switching median filter. For high noise densities, the method correctly detects up to 100% of noisy pixels.

Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images

A novel image filter based on type-2 fuzzy logic techniques is proposed for detail-preserving restoration of digital images corrupted by impulse noise. The performance of the proposed filter is evaluated for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise filters from the literature. Experimental results show that the proposed filter exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving thin lines, edges, texture, and other useful information within the image.

Impulse Noise Removal From Digital Images by a Detail-Preserving Filter Based on Type-2 Fuzzy Logic

An efficient approach for the removal of impulse noise from the corrupted image using neural network based impulse detector

This paper introduces a robust edge detection method that relies on an integrated process for denoising images in the presence of high impulse noise. This process is shown to be resilient to impulse (or salt and pepper) noise even under high intensity levels. The proposed switching adaptive median and fixed weighted mean filter (SAMFWMF) is shown to yield optimal edge detection and edge detail preservation, an outcome we validate through high correlation, structural similarity index, and peak signal-to-noise ratio measures. For comparative purposes, a comprehensive analysis of other denoising filters is provided based on these various validation metrics. The non-maximum suppression method and new edge following maximum-sequence are the two techniques used to track the edges and overcome edge discontinuities and noisy pixels, especially in the presence of high-intensity noise levels. After applying predefined thresholds to the grayscale image, and thus obtaining a binary image, several morphological operations are used to remove the unwanted edges and noisy pixels and perform edge thinning to ultimately provide the desired edge connectivity, which results in an optimal edge detection method. The obtained results are compared with other existing state-of-the-art denoising filters and other edge detection methods in support of our assertion that the proposed method is resilient to impulse noise even under high-intensity levels.

A Robust Edge Detection Approach in the Presence of High Impulse Noise Intensity Through Switching Adaptive Median and Fixed Weighted Mean Filtering

A new adaptive switching median filter is proposed to remove salt-and-pepper impulse noise from corrupted image. The algorithm is developed by combining advantages of the known median-type filters with impulse noise detection step. Comparison of the given method with traditional filters is provided. A visual example is given to demonstrate the performance of the proposed filter.

Image denoising using adaptive switching median filter

A new neural network adaptive switching median (NASM) filter is proposed to remove salt-and-pepper impulse noise from highly corrupted image The algorithm is developed by combining advantages of the known progressive median filter with impulse detection scheme Neural network was included into impulse detection step to improve its characteristics Comparison of the given method with traditional median-type filters is provided

Neural Network Adaptive Switching Median Filter for Image Denoising

A new neural network adaptive switching median (NASM) filter is proposed to remove salt-and-pepper impulse noise from corrupted image. The algorithm is developed by combining advantages of the known median-type filters with impulse detection scheme and the neural network was included into impulse detection step to improve its characteristics. Comparison of the given method with traditional filters is provided. A visual example is given to demonstrate the performance of the proposed filter.

Neural network adaptive switching median filter for the restoration of impulse noise corrupted images

A new neural network adaptive switching median (NASM) filter is proposed to remove salt-and-pepper impulse noise from corrupted image. The algorithm is developed by combining advantages of the known median-type filters with impulse detection scheme and the neural network was included into impulse detection step to improve its characteristics. Comparison of the given method with traditional filters is provided.

Pavel S. Zvonarev

Papers

Image denoising using adaptive switching median filter

Neural Network Adaptive Switching Median Filter for Image Denoising

Neural network adaptive switching median filter for the restoration of impulse noise corrupted images

Adaptive switching median filter with neural network impulse detection step