Removal Of High Density Impulse Noise Research Articles

DIBS: distance- and intensity-based separation filter for high-density impulse noise removal

DIBS: distance- and intensity-based separation filter for high-density impulse noise removal

A novel statistical golden ratio based adaptive high density impulse noise removal algorithm

A novel statistical golden ratio based adaptive high density impulse noise removal algorithm

Intensity bound limit filter for high density impulse noise removal

Intensity bound limit filter for high density impulse noise removal

High Density Impulse Noise Removal from Color Images by K-means Clustering based Detection and Least Manhattan Distance-oriented Removal Approach

Removal of impulse noise from color images is a stringent job in the arena of image processing. Impulse noise is fundamental of two types: Salt and pepper noise (SAPN) and Random valued impulse noise (RVIN). The key challenge in impulse noise removal from color images lies in tackling out the randomness in the noise pattern and in handling multiple color channels efficiently. Over the years, several filters have been designed to remove impulse noise from color images, but still, the researchers face a stringent challenge in designing a filter effective at high noise densities. In this study, a combination of K-means clustering-based detection followed by a minimum distance-based approach for removal is taken for high-density impulse noise removal from color images. In the detection phase, K-means clustering is applied on combined data consisting of elements from designated 5 × 5 windows of all the planes from RGB color images to segregate noisy and non-noisy elements. In the removal phase, noisy pixels are replaced by taking the average of medians of all non-noisy pixels and non-noisy pixels under 7 × 7 windows residing at least Manhattan distance from the inspected noisy pixel. Performance of the proposed method is evaluated and compared up against the latest filters, on the basis of well-known metrices, such as Peak signal to noise ratio (PSNR) and Structural similarity index measurement (SSIM). Based on these comparisons, the proposed filter is found superior than the compared filters in removing impulse noise at high noise densities.

High‐density impulse noise detection and removal using deep convolutional neural network with particle swarm optimisation

Most of the impulse denoisers are either median filter-based or fuzzy filter-based, which can only perform well in low noise conditions. This study presents an efficient convolutional neural network (CNN) with particle swarm optimisation (PSO) model for high-density impulse noise removal. The proposed high-density impulse noise detection and removal model mainly consists of two parts: the impulse noise removal and impulse noisy pixel detection for restoration. The authors' model initially leverages the powerful ability of deep CNN architecture to separate noise from the noisy image, then adopts PSO to pinpoint the most optimised threshold values for detecting impulse noisy pixels. An ensemble of these algorithms is an intelligent and adaptive solution, producing a clean output while preserving significant pixel information. Targeting to solve high-density impulse noise problems, the authors have trained their model with a massive collection of natural images and 14 standard testing images are used for validation purposes. In order to validate the robustness of the proposed method, different levels of high-density impulse noise are considered. Based on the final denoised images, their model has proven its reliability, in terms of both visual quality and quantitative evaluation, on greyscale and colour images.

Nonconvex Low Rank Approximation With Phase Congruency Regularization for Mixed Noise Removal

Mixed noise removal from a natural image is a challenging task since the complex noise distribution usually is inestimable. Many noise removal methods based on the low rank approximation have an excellent image denoising performance and are effective for recovering the images corrupted by Gaussian noise. These methods based on the additive white Gaussian noise(AWGN) model are sensitive to the outliers and non-Gaussian noise, such as the salt-and-pepper impulse noise (SPIN) and random valued impulse noise (RVIN). Such methods for mixed noise removal, however, are less effective in preserving image structures and tend to undesired staircase artifacts. This paper presents a novel Nonconvex Low Rank Model with Phase congruency and overlapping Group sparsity regularization (NLRM-PG) for mixed noise removal. Moreover, an efficient optimization algorithm under the alternating direction method of multipliers and majorization minimization framework is proposed to solve the NLRM-PG model. We demonstrate that the proposed method is effective for preserving local irregular structures and it reduces staircase artifacts with the two types of mixture noise, namely, AWGN+SPIN and AWGN+RVIN. Both qualitative and quantitative experiment results on synthetic noisy images and real noisy images illustrate that the proposed method can remove mixture noise in images more efficiently than the existing methods can do. And the results also outperform those obtained by using the competing state-of-the-art methods, particularly for the removal of high-density impulse noise.

A Novel Technique for Removal of High Density White Spot Noise from Digital Neutron Radiographic Images

This paper proposes a novel technique of a daptive s witching a lternative m edian (ASAM) filter for high-density white spot noise removal. The ASAM filter is composed of two blocks filtering, namely main and secondary block filtering, respectively. The proposed secondary block filtering is a new technique in high-density impulse noise removal and the main contribution of this research. The ASAM algorithm was tested on the standard 8-bit gray-scale, 512×512 pixel Lena image and a real neutron radiographic image. The results showed significant reduction of white spot noise in both types of images through visual inspection. To measure the performance of noise removal in simulation test we measured the p eak si gnal - to -n oise r atio (PSNR), s tructural s imilarity (SSIM) index , and denoising time, and in real application tests we measured s ignal - to -n oise r atio (SNR). From the experiments of simulation test, at the highest level noise of 95 % th e obtained PSNR and SSIM are 23.584 dB and 0.696 respectively. These are higher than the results of other algorithms that are 16.697 dB and 0.475 , respectively , for DBA, 16.696 dB and 0.408 for NAFSM, and 18.860 dB and 0.568 for NASNLM. The denoising time s for DBA, NAFSM, NASNLM , and ASAM we re obtained as 6.469 s, 5.186 s, 36.735 s, and 5.197 s respectively. From the experiments of real application test we obtained the SNR for DBA, NAFSM, NASNLM , and ASAM as 32.42 dB, 6.01 dB, 18.77 dB , and 32.96 dB , respectively. In general, these results show that ASAM filter is superior to the existing filtering methods. The ASAM filter improved the image restoration quality, especially in removing the high-density white spot noise, and was able to yield good filtering result w hic h exhibits better PSNR, SSIM, denoising time , and qualitative visual inspection .

Combination of adaptive vector median filter and weighted mean filter for removal of high‐density impulse noise from colour images

In this study, a combination of adaptive vector median filter (VMF) and weighted mean filter is proposed for removal of high-density impulse noise from colour images. In the proposed filtering scheme, the noisy and non-noisy pixels are classified based on the non-causal linear prediction error. For a noisy pixel, the adaptive VMF is processed over the pixel where the window size is adapted based on the availability of good pixels. Whereas, a non-noisy pixel is substituted with the weighted mean of the good pixels of the processing window. The experiments have been carried out on a large database for different classes of images, and the performance is measured in terms of peak signal-to-noise ratio, mean squared error, structural similarity and feature similarity index. It is observed from the experiments that the proposed filter outperforms (~1.5 to 6 dB improvement) some of the existing noise removal techniques not only at low density impulse noise but also at high-density impulse noise.

Adaptive Unsymmetrical Trim-Based Morphological Filter for High-Density Impulse Noise Removal

The modified decision-based unsymmetrical trimmed median filter (MDBUTMF), which is an efficient tool for restoring images corrupted with high-density impulse noise, is only effective for certain types of images. This is because the size of the selected window is fixed and some of the center pixels are replaced by a mean value of pixels in the window. To address these issues, this paper proposes an adaptive unsymmetrical trim-based morphological filter. Firstly, a strict extremum estimation approach is used, in order to decide whether the pixel to be processed belongs to a monochrome or non-monochrome area. Then, the center pixel is replaced by a median value of pixels in a window for the monochrome area. Secondly, a relaxed extremum estimation approach is used to control the size of structuring elements. Then an adaptive structuring element is obtained and the center pixel is replaced by the output of constrained morphological operators, i.e., the minimum or maximum of pixels in a trimmed structuring element. Our experimental results show that the proposed filter is more robust and practical than the MDBUTMF. Moreover, the proposed filter provides a preferable performance compared to the existing median filters and vector median filters for high-density impulse noise removal.

An Efficient Denoising Algorithm for Detection and Removal of High-Density Impulse Noise from Digital Images

Background/Objectives: Images that are affected by noise should be denoised in order to obtain information from it. In this paper an efficient denoising algorithm based on adaptive and unsymmetric trimmed median filter is proposed which effectively gets rid off the high-density salt and pepper noise. Methods/Stastical Analysis: The proposed algorithm restores the noisy pixel with the trimmed median value when, 0’s and 255’s are found in the neighborhood and also adaptively controls the neighborhood size based on the relative amount of noisy pixels in the selected neighborhood. Many of the existing algorithms do not preserve fine details of the image while removing noise at high noise densities. Results/ Findings: The Proposed Algorithm (PA) is tested against grayscale and color images and it gives an exceptional Peak Signal to Noise Ratio (PSNR) and Image Enhancement Factor (IEF) especially at higher noise densities. Conclusion/Application: This algorithm effectively removes high density noise caused by bit error transmission or introduced during the image acquisition stage.Keywords: Image Denoising, Impulse Noise, Median Filter, Mid Point Filter, Salt and Pepper Noise

A switching median–mean filter for removal of high-density impulse noise from digital images

A new, efficient switching median–mean filter is proposed to remove high density impulse noise from digital images. The proposed method consists of detection and filtering stages. The pixels that are labeled noise-free in the detection stage remain unchanged and the noisy-pixels are replaced by the reference image based the proposed filter with 3×3 window. The simulation results show that the proposed filter outperforms some existing methods, both in vision and quantitative measurements. Moreover, our method is not only efficient to remove high density impulse noise and preserve the details in the image, but also possess a short processing time.

Direction-based adaptive switching filter for removing high-density impulse noise

A direction-based adaptive switching (DBAS) filter is presented for the removal of high-density impulse noise in images. The extrema detection and 28-directional detection are employed to discriminate the pixels as noisy or noise-free. If a pixel is classified as noisy, it will be replaced by a median or a mean value within an adaptive filter window with respect to different noise densities. Simulation results show that the miss-detection ratio and false-alarm ratio are both very low even at noise level as high as 90%. At the same time, better results are obtained in terms of the qualitative and quantitative measures. The peak signal-to-noise ratios increase by nearly 1 dB compared with other existing algorithms. In addition, the computation time is around 10 s for test images with resolutions of 512 × 512 since the proposed approach has low complexity.

Removal of high-density impulse noise based on switching morphology-mean filter

In this paper, an effective switching morphology-mean filter is proposed to remove impulse noise from digital images. The proposed method consists of two stages. The first stage is to detect the impulse noise in an image. The second stage is to construct adaptive structuring elements and remove the impulse noise. In this stage, the “noise-pixels” are filtered out by using the morphology-mean filter, and the “noise-free pixels” are copied directly to the output image. The proposed filter uses the window of size 3×3pixels. Simulation results show that the proposed method outperforms some existing algorithms for high-density impulse noise, both in vision and quantitative measurements. Moreover, our method can preserve effectively the details in an image, and has less processing time.

A sparsity-ranking edge-preservation filter for removal of high-density impulse noises

In this study, a novel sparsity-ranking edge-preservation filter (SREPF) is proposed for removal of high-density impulse noise in images. Using the sparse matrix representation, the first stage of SREPF is not only to identify the noisy candidates but also to decide the processing order of them via a rank of noise-pixel sparsity in the working window. Then the second stage of SREPF utilizes a modified double Laplacian convolution to confirm the truly noisy pixels and yield a directional mean to recover them. This new approach has achieved more remarkable success rate of the edge detection than other edge-preservation methods especially in high noise ratio over 0.5. As a result, SREPF has significant improvements in terms of edge preservation and noise suppression exhibited by the peak signal-to-noise ratio (PSNR) and the structural similarity index metric (SSIM). Simulation results show that this method is capable of producing better performance compared to several representative filters.

Efficient Improvements on the BDND Filtering Algorithm for the Removal of High-Density Impulse Noise

Switching median filters are known to outperform standard median filters in the removal of impulse noise due to their capability of filtering candidate noisy pixels and leaving other pixels intact. The boundary discriminative noise detection (BDND) is one powerful example in this class of filters. However, there are some issues related to the filtering step in the BDND algorithm that may degrade its performance. In this paper, we propose two modifications to the filtering step of the BDND algorithm to address these issues. Experimental evaluation shows the effectiveness of the proposed modifications in producing sharper images than the BDND algorithm.

A new fuzzy-based decision algorithm for high-density impulse noise removal

This paper proposes a new efficient fuzzy-based decision algorithm (FBDA) for the restoration of images that are corrupted with high density of impulse noises. FBDA is a fuzzy-based switching median filter in which the filtering is applied only to corrupted pixels in the image while the uncorrupted pixels are left unchanged. The proposed algorithm computes the difference measure for each pixel based on the central pixel (corrupted pixel) in a selected window and then calculates the membership value for each pixel based on the highest difference. The algorithm then eliminates those pixels from the window with very high and very low membership values, which might represent the impulse noises. Median filter is then applied to the remaining pixels in the window to get the restored value for the current pixel position. The proposed algorithm produces excellent results compared to conventional method such as standard median filter (SMF) as well as some advanced techniques such as adaptive median filters (AMF), efficient decision-based algorithm (EDBA), improved efficient decision-based algorithm (IDBA) and boundary discriminative noise detection (BDND) switching median filter. The efficiency of the proposed algorithm is evaluated using different standard images. From experimental analysis, it has been found that FBDA produces better results in terms of both quantitative measures such as PSNR, SSIM, IEF and qualitative measures such as Image Quality Index (IQI).

An Iterative Bilateral Weighted Median filter for the removal of High-Density impulse noise

For removing the impulse noise with better edge and fine detail preservation, we propose an iterative bilateral weighted median (IBWM) filter. Instead of the specified weight of WM filters, we propose a novel weight fuction, bilateral weight (BW) where is the combination of domain weight (DW) in spatial domain and range weight (RW) in intensity domain. The DW is designed to give higher weight to pixels that are spatially close to a center pixel. And the RW is designed to give higher weight to pixels that are similar to the mean value of pixels in the filtering window. Finally, the rates of DW and RW are controlled according to noise distributions in a filtering window. For ensuring high accuracy of noise detection, the IBWM filter uses iterative process using increasing filtering window size. Simulation results indicate that our algorithm provides a significant improvement than any other existing techniques.