Random-valued Impulse Noise Research Articles

A Blind CNN Denoising Model for Random-Valued Impulse Noise

Denoising convolutional neural networks (DnCNNs), initially developed for Gaussian noise removal, are powerful nonlinear mapping models in image processing. After changes in training data, they can be used for suppression of random-valued impulse noise (RVIN) with excellent results. To achieve favorable denoising performance, however, it is necessary to have an accurate perception of the noise ratio so that the most suitable DnCNN can be chosen for denoising. Thus, this model is severely limited in flexibility. To address this problem, we propose a blind CNN model for RVIN denoising with a flexible noise ratio predictor (NRP) as an indicator. Some patches are randomly selected from the RVIN-corrupted test image, and feature vectors that indicate whether the center pixel is contaminated or not are extracted by the predictor. These feature vectors are composed of multiple statistics, namely, the multiple rank-ordered absolute differences (ROADs), the clean pixel median deviation (CPMD), and the edge pixel difference (EPD). They are rapidly mapped to noise/clean (1 for noise, 0 for clean) labels by the pre-trained noise detector (the key component of our NRP). According to the ratio of the obtained noisy labels to the total number of selected patches, the predictor provides the noise ratio of the whole image. From the output of the NRP, i.e., the predicted noise ratio, the most appropriate DnCNN specifically trained for this noise ratio is exploited for denoising. Under the guidance of the NRP, the proposed method has the ability to handle unknown noise ratios. Simulation results indicate that our blind denoising CNN model achieves state-of-the-art performance in terms of both execution efficiency and restoration results.

Two-stage multi-layer perceptron estimation for random-valued impulse noise ratio

Two-stage multi-layer perceptron estimation for random-valued impulse noise ratio

Detail-preserving switching algorithm for the removal of random-valued impulse noise

This paper presents a new algorithm for the denoising of images corrupted with random-valued impulse noise (RVIN). It employs a switching approach that identifies the noisy pixels in the first stage and then estimates their intensity values to restore them. Local statistics of the textons in distinct orientations of the sliding window are exploited to identify the corrupted pixels in an iterative manner; using an adaptive threshold range. Textons are formed by using an isometric grid of minimum local distance that preserves the texture and edge pixels of an image, effectively. At the noise filtering stage, fuzzy rules are used to obtain the noise-free pixels from the proposed tri-directional pixels to estimate the intensity values of identified corrupted pixels. The performance of the proposed denoising algorithm is evaluated on a variety of standard gray-scale images under various intensities of RVIN by comparing it with state-of-the-art denoising methods. The proposed denoising algorithm also has robust denoising and restoration power on biomedical images such as, MRI, X-Ray and CT-Scan. The extensive simulation results based on both quantitative measures and visual representations depict the superior performance of the proposed denoising algorithm for various noise intensities.

Impulse noise reduction in audio signal through multi-stage technique

The fundamental aim of this study is exclusively focused upon detecting and restoring audio signals corrupted by random-valued impulse noise. This study is stemmed from a process in which the noisy samples are in depth analysed through a cascade of stages. In each stage, a vector of different length is used and the similarity degree between the tested sample and its neighbors are precisely measured in each vector. The tested sample which achieves the minimum degree of similarity with its neighboring samples in each stage is highly regarded as an original sample. Several experiments are carried out on different audio sounds to assess the performance of the proposed algorithm in terms of peak signal to noise ratio (PSNR) and sound quality. The attained results have shown that the new method provides remarkable performance if compared to other well-known algorithm, particularly at low noise rates. The method adopted has turned out to be more successful in removing noisy samples and keeping the original ones intact. Also the method in question highlights low computational complexity and easy to implement.

RNLp: Mixing Nonlocal and TV-Lp Methods to Remove Impulse Noise from Images

We propose a new variational framework to remove random-valued impulse noise from images. This framework combines, in the same energy, a nonlocal $$L^p$$ data term and a total variation regularization term. The nonlocal $$L^p$$ term is a weighted $$L^p$$ distance between pixels, where the weights depend on a robust distance between patches centered at the pixels. In a first part, we study the theoretical properties of the proposed energy, and we show how it is related to classical denoising models for extreme choices of the parameters. In a second part, after having explained how to numerically find a minimizer of the energy thanks to primal-dual approaches, we show extensive denoising experiments on various images and noise intensities. The denoising performance of the proposed methods is on par with state-of-the-art approaches, and the remarkable fact is that, unlike other successful variational approaches for impulse noise removal, they do not rely on a noise detector.

Convolutional neural network-based detector for random-valued impulse noise

Convolutional neural network-based detector for random-valued impulse noise

Region Adaptive Fuzzy Filter: An Approach for Removal of Random-Valued Impulse Noise

This paper proposes region adaptive fuzzy filter for removal of random-valued impulse noise (RVIN) from color images. It is observed from existing literature that the filter performance increases with improved accuracy of noise detection and better adaption of the filter parameters. Improved minimum mean value detection mechanism is proposed for better classification of noisy and nonnoisy pixels in context to the removal of RVIN from color images. The modified fuzzy filter considers the correlation among the color channels and recursively adapts itself in accordance with the local noise densities. This filter incorporates an adaption technique to determine the maximum allowable window size used during fuzzification and filtering. Rather than one efficient filtering step that has a potential for losing information, region selective second iteration of the filter is applied on highly corrupted regions so as to preserve more image details. Experimental results suggest that the proposed filter provides improved performance in terms of peak signal to noise ratio, normalized color difference, structural similarity index, feature similarity index for color images, and perceptual similarity than most of the state-of-the-art filters.

Difference based median filter for removal of random value impulse noise in images

Random value impulse noise of images has many sources, such as image sensor, electronic components, etc. How to removal of noise and restore degraded image is always an interesting problem. The decision based algorithms as efficient methods to suppress noise have been extensively studied for a long time. In this type of algorithms, the first step is to classify the corrupted pixels from the surroundings, but it is not an easy thing since each image is different. The efficiency of the classification has great influence on the overall performances of the algorithms. A difference based median filter which can efficiently locate the random value impulse noise is proposed in this paper. Based on this filter, a new algorithm for removal of impulse noise in images is designed. A comparison of the performances is made among several existing algorithms in term of Image Enhancement Factor, Peak Signal-to-Noise Ratio and Structure Similarity Index. Finally, the proposed method is used for underwater image processing to suppress the random value impulse noise modified by Histogram Equalization operation. Visual and quantitative results indicate that the proposed method outperforms most of algorithms for removal of impulse noise in literatures.

Sparse representation based image deblurring model under random-valued impulse noise

In this article, we introduce a new patch-based model for restoring images simultaneously corrupted by blur and random-valued impulse noise. The model involves a $$l_0$$ -norm data-fidelity term, a sparse representation prior over learned dictionaries, and the total variation (TV) regularization. Unlike previous works Cai et al. (Inverse Probl Imaging 2(2):187–204, 2008), Ma et al. (SIAM J Imaging Sci 6(4):2258–2284, 2013), one-phase approach is utilized for random-valued impulse noise. As in Yuan and Ghanem (IEEE conference on computer vision and pattern recognition (CVPR), pp 5369–5377, 2015), the $$l_0$$ data-fitting term plays an influential role for removing random-valued impulse noise. Moreover, the sparse representation prior enables to preserve textures and details efficiently, whereas TV regularization locally smoothes images while keeping sharp edges. To handle nonconvex and nondifferentiable terms, we adopt a variable splitting scheme, and then the penalty method and alternating minimization algorithm are employed. This results in an efficient iterative algorithm for solving our model. Numerical results are reported to show the effectiveness of the proposed model compared with the state-of-the-art methods.

Texture-oriented image denoising technique for the removal of random-valued impulse noise

An iterative texture-oriented image denoising technique is proposed for the restoration of images corrupted with random-valued impulse noise (RVIN). The proposed technique opts a switching approach that first identifies the pixels corrupted by RVIN and then estimates their intensity values to restore the images. Textons of distinct orientations conforming to bilateral symmetry are proposed for the identification of corrupted pixels. To estimate the intensity values of the identified corrupted pixels, the textons having local similarity are used. As the textons are fundamental elements of texture perception, the proposed technique preserves the texture information of images, effectively. The performance of the proposed denoising technique is evaluated on standard benchmark test images under various intensities of RVIN by comparing it with state-of-the-art techniques. The simulation results depict the significant performance of the proposed denoising technique for low as well as higher intensities of RVIN.

Removal of Random Valued Impulse Noise with Modified Weighted Mean Filter by using Denoising Algorithm: Emerging Opportunities of Image Processing Application

Removal of Random Valued Impulse Noise with Modified Weighted Mean Filter by using Denoising Algorithm: Emerging Opportunities of Image Processing Application

Triple Threshold Statistical Detection filter for removing high density random-valued impulse noise in images

This study presents a novel noise detection algorithm which satisfactorily detects noisy pixels in images corrupted by random-valued impulse noise of high levels up to 80% noise density. Three levels of adaptive thresholds along with an auxiliary condition are used in this method which adequately addresses the drawbacks of existing methods, especially the miss detection of noise-free pixels as noisy pixels and vice versa. A noise signature is calculated for every pixel and compared with the first threshold to identify noise followed by the comparison of the central pixel with the second and third levels of thresholds. In addition to the standard deviation and mean, the concept of quartile has been used as another measure of dispersion. After detection, a fuzzy switching weighted median filter is applied to restore the corrupted image. The simulation results demonstrate that the proposed method is able to outperform the existing methods in both the detection and filtering of random-valued impulse noise in images.

An Efficient Random Valued Impulse Noise Suppression Technique Using Artificial Neural Network and Non-Local Mean Filter

A new technique for suppression of Random valued impulse noise from the contaminated digital image using Back Propagation Neural Network is proposed in this paper. The algorithms consist of two stages i.e. Detection of Impulse noise and Filtering of identified noisy pixels. To classify between noisy and non-noisy element present in the image a feed-forward neural network has been trained with well-known back propagation algorithm in the first stage. To make the detection method more accurate, Emphasis has been given on selection of proper input and generation of training patterns. The corrupted pixels are undergoing non-local mean filtering employed in the second stage. The effectiveness of the proposed technique is evaluated using well known standard digital images at different level of impulse noise. Experiments show that the method proposed here has excellent impulse noise suppression capability.

Adaptive Partition-Cluster-Based Median Filter for Random-Valued Impulse Noise Removal

As the most popular nonlinear denoise technique, the median filter has attracted significant attention in recent years. In this paper, a novel adaptive median filter is presented to remove random-valued impulse noise in images, named Adaptive Partition-Cluster-Based Median (APCM) Filter. Based on the partition cluster idea, the noise detector classifies pixels into different groups and identifies the noisy pixels in different regions adaptively without iterations. According to the results of noise detection, an improved adaptive decision-based filter is presented to restore the pixels which are corrupted by random-valued impulse noise. The proposed filter technique is open to any impulse noise. Extensive simulation results demonstrate that the proposed method substantially outperforms other state-of-the-arts impulse noise filter techniques both visually and in terms of objective quality measures. Furthermore, the proposed method is much friendly to the hardware parallel implementation of image processing because of its low computation complexity and simple realizable structure.

Structure-Adaptive Fuzzy Estimation for Random-Valued Impulse Noise Suppression

Noise detection accuracy is crucial in suppressing random-valued impulse noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued impulse noise points. This paper proposes an iterative structure-adaptive fuzzy estimation (SAFE) for random-valued impulse noise suppression. This SAFE method is developed in the framework of Gaussian maximum likelihood estimation. The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as noise free) is estimated via a novel-minimal-path-based structure propagation to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the reestimation error of the uncorrupted intensity information. The comparative experimental results show that the proposed structure-adaptive fuzziness can lead to effective restoration. An efficient implementation of this SAFE method is also realized via graphics-processing-unit-based parallelization.

Random-valued impulse noise removal using adaptive ranked-ordered impulse detector

This paper proposes an adaptive rank-ordered impulse detector based on local statistics for identifying random-valued impulse noise (RVIN) pixels. A piecewise power function is applied to the rank-ordered statistic so as to enlarge the difference between noisy pixels and noise-free pixels. The proposed detector performs well over most well-known detectors. By combining the noise detector with an improved edge-preserving regularization filter, a two-stage iterative denoising procedure is performed to remove RVIN. An effective and robust adaptive stop criterion is also proposed based on the number of restored pixels for the iterative running of the filtering process. Experimental results indicate that the proposed denoising algorithm achieves a satisfying performance in terms of quantitative evaluation and visual effect.

Universal Impulse Noise Suppression Using Extended Efficient Nonparametric Switching Median Filter

This paper presents a filtering algorithm called extended efficient nonparametric switching median (EENPSM) filter. The proposed filter is composed of a nonparametric easy to implement impulse noise detector and a recursive pixel restoration technique. Initially, the impulse detector classifies any possible impulsive noise pixels. Subsequently, the filtering phase replaces the detected noise pixels. In addition, the filtering phase employs fuzzy reasoning to deal with uncertainties present in local information. Contrary to the existing conventional filters that only focus on a particular impulse noise model, the EENPSM filter is capable of filtering all kinds of impulse noise (i.e. the random-valued and/or fixed-valued impulse noise models). Extensive qualitative and quantitative evaluations have shown that the EENPSM method performs better than some of the existing methods by giving better filtering performance.

Random Valued Impulse Noise Removal Using Region Based Detection Approach

Removal of random valued noisy pixel is extremely challenging when the noise density is above 50%. The existing filters are generally not capable of eliminating such noise when density is above 70%. In this paper a region wise density based detection algorithm for random valued impulse noise has been proposed. On the basis of the intensity values, the pixels of a particular window are sorted and then stored into four regions. The higher density based region is considered for stepwise detection of noisy pixels. As a result of this detection scheme a maximum of 75% of noisy pixels can be detected. For this purpose this paper proposes a unique noise removal algorithm. It was experimentally proved that the proposed algorithm not only performs exceptionally when it comes to visual qualitative judgment of standard images but also this filter combination outsmarts the existing algorithm in terms of MSE, PSNR and SSIM comparison even up to 70% noise density level.

Random Valued Impulse Noise Removal Using Region Based Detection Approach

Random Valued Impulse Noise Removal Using Region Based Detection Approach

Some studies on detection and filtering algorithms for the removal of random valued impulse noise

Removing random valued impulse noise (RVIN) is a challenging task in corrupted images. This article aims to study some detection and filtering algorithms which remove RVIN in images. In addition to some state-of-the-art detection and filtering algorithms, a new detection technique, measures of dispersion (MOD) algorithm, for removing very high-density RVIN proposed by authors is also compared with existing methods. In the detection stage, rank order absolute difference, rank order logarithmic difference, adaptive switching median, triangle-based linear interpolation, and MOD algorithms are considered. Median filter, fuzzy switching median filter, and fuzzy switching weighted median filter are used for filtering followed by the detection algorithms. Comparative studies in terms of peak signal-to-noise ratio and structural similarity have been devised to evaluate the performance of various filtering schemes.