Filter For Impulse Noise Removal Research Articles

Extended neighborhood-based road and median filter for impulse noise removal from depth map

In recent years there has been growing interest in the study of depth map's impulse noise detection and removal. The Rank-ordered Absolute Differences (ROAD) based on neighborhood statistics can effectively detect impulse noise for RGB images. However, since the statistical object of the ROAD is each pixel value in the neighborhood, and the neighborhood may contain other impulse noise, invalid depth zero-pixel points and other singular points, especially for large areas with depth missing, it is difficult for the algorithm to ensure the correctness and accuracy of the detection results. Therefore, the ROAD algorithm is not fully applicable to depth maps. Similarly, the median filter, as an effective impulse noise filtering algorithm, also has the same deficiency, so it is not suitable for areas in depth maps with no depth value. To address these issues, we present here an Extended Neighborhood-based ROAD and Median Filter algorithm to remove impulse noise from depth map. This approach improves the functionality of the ROAD algorithm and the Median Filter by extending the neighborhood of the current pixel adaptively, shielding zero-valued pixels and filtering out detected impulse noises in real time during detection, therefore effectively improving the accuracy of impulse noise filtering. It consequently reduces the miss rate and the error detection rate, and ultimately achieves a better edge-preserving and denoising effect. Our empirical evidence shows that the proposed algorithm outperforms existing denoising algorithms on both quantitative measures and visual perception qualities.

A New Spatial Domain Filter for Impulse Noise Removal with Improved Accuracy Based on Multiple Conditions

Numerous filtering methods are proposed for Impulse noise removal, it is an important task in the field of image restoration. The familiar spatial domain algorithm to remove impulse noise is Standard Median Filter (SMF). Most of the existing algorithms are based on median filtering and recent algorithms are Modified Hybrid Median Filter (MHMF) and New Modified Hybrid Median Filter (NMHMF). These two are worked up to 20% noise density. In this paper proposed a new` algorithm for impulse noise removal above 20% noise density conditions with different samples of images. The implementation of proposed method compares with known existing methods by comparing Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).

Adaptive switching filter for impulse noise removal in digital content

In this paper, a new fuzzy-rule-based impulse noise denoising method that removes unwanted artifacts and reconstructs original images is proposed. The proposed method is based on the fuzzy rule selective approach, which has four sub-methods. The proposed method can effectively remove noise artifacts that are caused by various levels of impulse noise. Simulation results show that the presented method outperforms conventional methods. The proposed method can be directly applied to various consumer electronics displays.

An adaptive dynamically weighted median filter for impulse noise removal

A new impulsive noise removal filter, adaptive dynamically weighted median filter (ADWMF), is proposed. A popular method for removing impulsive noise is a median filter whereas the weighted median filter and center weighted median filter were also investigated. ADWMF is based on weighted median filter. In ADWMF, instead of fixed weights, weightages of the filter are dynamically assigned with the results of noise detection. A simple and efficient noise detection method is also used to detect noise candidates and dynamically assign zero or small weights to the noise candidates in the window. This paper proposes an adaptive method which increases the window size according to the amounts of impulsive noise. Simulation results show that the AMWMF works better for both images with low and high density of impulsive noise than existing methods work.

Context model based edge preservation filter for impulse noise removal

In this article, a new edge preserving contextual model based image restoration technique is proposed for images affected by impulse noise. The proposed restoration technique consists of two stages: noisy pixel identification and restoration. Center sliding window is considered as current processing pixel for both noisy pixel identification and restoration. In the first stage of the proposed technique, we follow an absolute directional difference of the neighborhood pixels to identify the pixels those are affected by impulse noise. We propose an edge preserving contextual model to restore the noisy pixels. The noise correction stage of the proposed scheme depends on the context model of the noise-free pixels in the selected window. The parameters of the contextual model are obtained using a Gaussian kernel. The proposed algorithm is tested on nine benchmark test images. The evaluation of the proposed algorithm is carried out by comparing it against nine competitive state-of-the-art algorithms for impulse noise removal. The proposed algorithm is evaluated using Peak Signal to Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM), Non-shifted Edge Ratio (NSER) and Correlation Factor (CF) performance measures. Experimental results corroborate that the proposed algorithm provides better performance than the existing state-of-art impulse denoising methods.

A new cluster based adaptive fuzzy switching median filter for impulse noise removal

A new clustering technique based on most allied directional neighbors is proposed to suppress low and high-density impulse noise from digital images. Most allied neighbors exhibit a vital role in estimation as well restoration of appropriate gray level value of corrupted pixels. In first phase, most allied directional neighbors, i.e., pixels directly attached to central pixel and the directional pixels (horizontal, vertical and two diagonal directions) next to attached pixels in the processing window are partitioned into two equal size clusters based on gradient values. Cluster with a minimum sum of gradient values (most similar neighbors) and the one with relatively large gradient values are passed to fuzzy inference system to infer the current pixel to be noisy-free, edge or a noisy. In second phase, a switching technique opts one of the three options depending upon fuzzy membership degrees and local information to restore the corrupted pixel value. A non-parametric approach based on local information for dynamic threshold setting using fuzzy logic makes the proposed filter computationally effective and adaptive to process a large number of images without user-defined parameters. The proposed algorithm is simple to implement and simulation results based on well know quantitative measures indicate the supremacy of the proposed filter for random-valued impulse noise as well as salt and peppers noise.

Adaptive fuzzy inference system based directional median filter for impulse noise removal

Noise filtering in presence of important image detail information is considered as challenging task in imaging applications. Use of fuzzy logic based techniques is capturing more focus since last decade to deal with these challenges. In order to tackle conflicting issues of noise smoothing and detail preservation, this paper presents a novel approach using adaptive fuzzy inference system for random valued impulse noise detection and removal. The proposed filter uses the intensity based directional statistics to construct adaptive fuzzy membership functions which plays an important role in fuzzy inference system. Fuzzy inference system constructed in this way is used by the noise detector for accurate classification of noisy and noise-free pixels by differentiating them from edges and detailed information present in an image. After classification of pixels, noise adaptive filtering is performed based on median and directional median filter using the information provided by the noise detector. Simulation results based on well known quantitative measure i.e., peak-signal-to-noise ratio (PSNR) show the effectiveness of proposed filter.

An adaptive-size median filter for impulse noise removal using neural network-based detector

In this paper, a neural network-based Adaptive-Size Median (ASMED) filter is proposed for impulse noise removal. This method consists of two stages: noise detection and noise filtering. Noise detection is performed by a neural network-based detector, and filtering is applied only to corrupt pixels in the noisy image. Extensive experimental analysis shows that the proposed technique can be used for images with different impulse noise models. Both quantitative and qualitative analyses show the superiority of the proposed filter over other existing filters. The proposed method is applied to both colour images and greyscale images, and better results are obtained.

An adaptive-size median filter for impulse noise removal using neural network-based detector

An adaptive-size median filter for impulse noise removal using neural network-based detector

Switching median and morphological filter for impulse noise removal from digital images

A switching median and morphological filter is presented for removing impulse noise. In the proposed filter, the noise detector is first adopted to identify noise pixels by combining the morphological gradient based on the erosion and dilation operators with the top-hat transform. Then the detected impulses are removed by the hybrid filter, which combines the improved median filter using only the noise-free pixels with the conditional morphological filter using the improved morphological operations. Extensive simulations demonstrate that the proposed filter can realize accurate noise detection and it has significantly better restoration performance than a number of decision-based filters at the various noise ratios.

Predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector

A predictive-based adaptive switching median filter for impulse noise removal using neural network-based noise detector (PASMF) is presented. The PASMF has a noise detector stage and a noise filtering stage. The noise detector implemented using feed forward neural network detects impulse noises in the corrupted image. The filter is a modified median filter, which removes detected impulse noise from the image. In contrast to the standard median filter, the PASMF computes the median value after predicting the appropriate values for neighboring corrupted pixels of the current central pixel in the filtering window. The results show that the PASMF gives better performance visually as well as in terms of different performance measures.

Novel method for removal of multichannel impulse noise based on half-space deepest location

A novel method is proposed for removing impulse (random-valued and salt-and-pepper) noise from multichannel digital images based on an adapted version of the DEEPLOC algorithm introduced by A. Struyf and P. J. Rousseeuw for calculation of approximate half-space (Tukey's) deepest location (median) in multivariate case. Due to its intrinsic multivariate/multidimensional nature, the proposed method eliminates the noise simultaneously on all channels without their separation, which preserves the spectral correlation between channels in a multichannel image. Denoising results of this new nonlinear spatial domain filter applied to benchmark images outperform currently used state-of-the-art filters for impulse noise removal from multichannel images in terms of both objective effectiveness criteria [peak-signal-to-noise-ratio (PSNR), mean absolute error (MAE), and normalized color distance (NCD)] and visual quality. The proposed filter successfully preserves the edges and fine image details, and is very effective for removal of medium and heavy multichannel impulse noise.

An Efficient Switching Median Filter Based on Local Outlier Factor

An effective algorithm for removing impulse noise from corrupted images is presented under the framework of switching median filtering. Firstly, noisy pixels are distinguished by Local Outlier Factor incorporating with Boundary Discriminative Noise Detection (LOFBDND). Then, the directional weighted median filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its neighbors in the filtering window. Our noise detection algorithm makes the decision so accurate that the miss detection rate and false detection rate are very low. Extensive simulation results show that our method provides better performance in terms of PSNR and MAE than many other median filters for impulse noise removal.

A Novel Spiking Cortical Model based Filter for Impulse Noise Removal

A novel spiking cortical model based switching mean filter for removing impulse noise is presented. In the proposed filter, the noise detector using spiking cortical model is first adopted to identify the pixels that are likely to be corrupted by impulse noise. Then the detected impulses are removed by the weighted mean filter while the noise-free pixels are left unaltered. Extensive simulations show that the proposed filter outperforms a number of existing decision-based filters due to its excellent performance in terms of effectiveness in image restoration. Because of its outstanding restoration performance, the proposed filter can be used for noise removal in numerous consumer electronics products such as digital camera and digital television.

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

Modified switching median filter for impulse noise removal

In this paper, a modified switching median filter is presented for noise reduction in color images that are corrupted with impulse (salt and pepper) noise. It is a two-phase noise detector: in the first phase, we use the adaptive vector median filter detection method to identify pixels that are likely to have been corrupted by noise (as noise candidates); in the second phase, these noise candidates are judged by using four one-dimensional Laplacian operators, which allow edge pixels to be preserved. Extensive experiments show that the proposed method outperforms many existing vector filters in terms of filtering performance. In particular, the proposed approach can effectively preserve thin lines, fine details, and image edges.

High performance detection filter for impulse noise removal in images

A high performance detection (HPD) filter is proposed for impulse noise removal in images. In this approach, the noisy pixels are detected iteratively through several phases, based on a set of unique similarity criteria. Simulation results show that the HPD filter outperforms others at medium to high noise rates and suppresses impulse noise effectively while preserving image details, even thin lines.

Hybrid neuro-fuzzy filter for impulse noise removal

A new filter is presented for images which are highly corrupted by impulse noise. The proposed operator is based on a hybrid neuro-fuzzy approach. The network structure of the filter is specifically designed to detect different patterns of noisy pixels typically occurring in highly corrupted data. The fuzzy mechanism embedded in the network aims at removing noise pulses without destroying fine details and textures. A learning method based on the genetic algorithms is adopted to adjust the network parameters from a set of training data. Experimental results show that the neuro-fuzzy filter is able to yield a very effective noise cancellation and to perform significantly better than state-of-the-art operators in the literature.