Modified Decision Based Unsymmetric Trimmed Median Filter Research Articles

Removal of Salt and Pepper Noise Using Hybrid Adaptive Switching Median Filter with Ant Colony Optimization Technique in Nano Electronic Applications

Background: Restoration of noisy images from the salt and pepper noise is an interesting area in the field of image processing. The restoration process can be done using various filtering algorithms. The restoration process should not affect the pixels of the original image. The problem of the existing work persists as the increase in the error rate while the dimensions as well as the image format changes. The proposed work consists of Hybrid Adaptive Switching median filtering (HASMF). The hybrid technique corrupted images’ high-density salt and pepper noise removal using Ant colony Optimization technique. This hybrid technique would remove the high-density salt and pepper noise from the corrupted images. The noisy pixel value from the corrupted images is identified and selected using the Ant Colony Optimization technique (ACO). The identified corrupted value can be replaced using the Adaptive Switching Median Filter. The switching process is carried out using the pixel by pixel with the normalized median values. The noisy pixels are identified and selected using Ant colony Optimization. The optimized values are subjected to the filtering process. The proposed method decreases the salt and pepper noise within the original image. The hybrid design approach was used in the proposed study, which used 45 nm technology combined with a Verilog-A model-based circuit that was implemented using Spintronic. It was discovered that the suggested changed task had less latency, used less space, and dissipated less power than the original. Furthermore, it was discovered that designed memory arrays were both energy and space-efficient. It does not affect the normal pixels within the original image. The comparison process has been made with the various existing algorithms such as Median Filter (MF), Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The proposed method has overcome the various performance metrics such as Peak Signal Noise Ratio (PSNR), Mean Square Error (MSE), and Structural Similarity Index (SSIM). The results obtained have shown the significant results in terms of object measures as well as visual perception of the denoised image.

Adaptive Trimmed Median Filter for Impulse Noise Detection and Removal with an Application to Mammogram Images

Mammography imaging is one of the most widely used techniques for breast cancer screening and analysis of abnormalities. However, due to some technical difficulties during the time of acquisition and digital storage of mammogram images, impulse noise may be present. Therefore, detection and removals of impulse noise from the mammogram images are very essential for early detection and further diagnosis of breast cancer. In this paper, a novel adaptive trimmed median filter (ATMF) is proposed for impulse noise (salt & pepper (SNP)) detection and removal with an application to mammogram image denoising. Automatic switching mechanism for updating the Window of Interest (WoI) size from ([Formula: see text]) to ([Formula: see text]) or ([Formula: see text]) is performed. The proposed method is applied on publicly available mammogram images corrupted with varying SNP noise densities in the range 5%–90%. The performance of the proposed method is measured by various quantitative indices like peak signal to noise ratio (PSNR), mean square error (MSE), image enhancement factor (IEF) and structural similarity index measure (SSIM). The comparative analysis of the proposed method is done with respect to other state-of-the-art noise removal methods viz., standard median filter (SMF), decision based median filter (DMF), decision based unsymmetric trimmed median filter (DUTMF), modified decision based unsymmetric trimmed median filter (MDUTMF) and decision based unsymmetric trimmed winsorized mean filter (DUTWMF). The superiority of the proposed method over other compared methods is well evident from the experimental results in terms of the quantitative indices (viz., PSNR, IEF and SSIM) and also from the visual quality of the denoised images. Paired t-test confirms the statistical significance of the higher PSNR values achieved by the proposed method as compared to the other counterpart techniques. The proposed method turned out to be very effective in denoising both high and low density noises present in (mammogram) images.

Deep Weber Dominant Local Order Based Feature Generator and Improved Convolution Neural Network for Brain Tumor Segmentation in MR Images

This paper introduces a scheme for retrieving deep features to carry out the procedure of recognising brain tumors from MR image. Initially, the MR brain image is denoised through the Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) after that the contrast of the image is improved through Contrast Limited Adaptive Histogram Equalization (CLAHE). Once the pre-processing task is completed, the next phase is to extract the feature. In order to acquire the features of pre-processed images, this article offers a feature extraction technique named Deep Weber Dominant Local Order Based Feature Generator (DWDLOBFG). Once the deep features are retrieved, the next stage is to separate the brain tumor. Improved Convolution Neural Network (ICNN) is used to achieve this procedure. To explore the efficiency of deep feature extraction and in-depth machine learning methods, four performance indicators were used: Sensitivity (SEN), Jaccard Index (JI), Dice Similarity Coefficient (DSC) and Positive Predictive Value (PPV). The investigational outputs illustrated that the DWDLOBFG and ICNN achieve best outputs than existing techniques.

Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal

In this study, we propose a new method, i.e. Adaptive Riesz Mean Filter (ARmF), by operationalizing pixel similarity for salt-and-pepper noise (SPN) removal. Afterwards, we compare the results of ARmF, A New Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Based on Pixel Density Filter (BPDF), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision-Based Algorithm (DBA) by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Image Enhancement Factor (IEF), and Visual Information Fidelity (VIF) for 20 traditional test images (Lena, Cameraman, Barbara, Baboon, Peppers, Living Room, Lake, Plane, Hill, Pirate, Boat, House, Bridge, Elaine, Flintstones, Flower, Parrot, Dark-Haired Woman, Blonde Woman, and Einstein), 40 test images in the TESTIMAGES Database, and 200 RGB test images from the UC Berkeley Dataset ranging in noise density from 10% to 90%. Moreover, we compare the running time of these algorithms. These results show that ARmF outperforms the methods mentioned above. We finally discuss the need for further research.

An efficient denoising of impulse noise from MRI using adaptive switching modified decision based unsymmetric trimmed median filter

Noise reduction in digital images is the eminent preprocessing scheme in major image processing activities which includes medical image segmentation. A well designed noise reduction scheme for highly corrupted medical images is needed for medical diagnosing schemes with an immediate attention. This paper proposes a novel Adaptive Switching Modified Decision Based Unsymmetric Trimmed Median Filter (ASMDBUTMF) for noise reduction in gray scale MR Images which are affected by salt and pepper noise. The Proposed method is the extension work of the existing Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) method. In this paper noisy pixels are detected using the occurrence of intensity values 0’s and 255’s. The noisy pixels are converted into noise-free pixels using trimmed median value s. The MDBUTMF Filter uses 3x3 size windows only while the proposed method uses window sizes based on Adaptive switching method to retain more details for noise reduction. The proposed ASMDBUTMF algorithm is compared with existing methods, namely Median Filter (MF), Rank Order Median Filter (ROMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Geometric Multiscale Ridgelet Support Vector Transform Filter (GMRSVTF) simultaneously. The new ASMDBUTMF Filter supports noise reduction for highly corrupted images with better peak signal-to-noise Ratio (PSNR) and Image Enhancement Factor (IEF).

Enhanced Modified Decision-Based Unsymmetric Trimmed Adaptive Neighborhood Winsorized Mean Filter for Removing 1–99% Levels of Salt-and-Pepper Noise

In this paper, a novel and effective algorithm for removing 1–99% levels of salt-and-pepper noise is proposed. The proposed algorithm comprises a two-phase scheme. The first phase involves a combination of enhanced modified decision-based unsymmetric trimmed mean filter (EMDBUTMF) and decision-based unsymmetrical trimmed modified winsorized mean filter (DBUTWMF), and the second phase is employed after the first phase to replace the left over noisy pixels. The proposed algorithm is the fusion of the benefits of EMDBUTMF, DBUTWMF and decision-based diagonal neighborhood pixel algorithm and shows better results than the decision-based algorithm, modified decision-based unsymmetric trimmed median filter (MDBUTMF), EMDBUTMF, Modified decision-based unsymmetrical trimmed median filter with trimmed global mean (MDBUTMF_GM), DBUTWMF. The proposed algorithm is examined against 1–99% levels of salt-and-pepper noise for several grayscale bitmap images, and it gives better peak-signal-to-noise ratio, image enhancement factor and Structural Similarity Index Measures values for high noise densities.

Comparative Study of Noise Removal Techniques with Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF)

Comparative Study of Noise Removal Techniques with Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF)

A new method based on pixel density in salt and pepper noise removal

In this paper, we deliver a new method to remove salt and pepper noise, which we refer to as based on pixel density filter (BPDF). The first step of the method is to determine whether or not a pixel is noisy, and then we decide on an adaptive window size that accepts the noisy pixel as the center. The most repetitive noiseless pixel value within the window is set as the new pixel value. By using 18 test images, we give the results of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), image enhancement factor (IEF), standard median filter (SMF), adaptive median filter (AMF), adaptive fuzzy filter (AFM), progressive switching median filter (PSMF), decision-based algorithm (DBA), modified decision-based unsymmetrical trimmed median filter (MDBUTMF), noise adaptive fuzzy switching median filter (NAFSM), and BPDF. The results show that BPDF produces better results than the above-mentioned methods at low and medium noise density.

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.

High Density Salt and Pepper Noise Removal in Images Using Adapted Decision Based Unsymmetrical Trimmed Mean Filter Cascaded with Gaussian filter

An adapted decision based unsymmetrical trimmed mean filter cascaded with Gaussian filter (ADCG) algorithm for the retrieval of gray scaled image which is induced by a very high density Salt and Pepper (impulse) noise is proposed and tested in this paper. The proposed calculation supplants the noisy pixel by trimmed mean quality when other pixel values, 0's and 255's are available in the selected window and when all the pixel qualities are 0's and 255's then the noisy pixel is supplanted by mean estimation of the considerable number of components present in the selected window. This proposed methodology shows preferred results over the Standard Median Filter (MF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), Progressive Switched Median Filter (PSMF) and Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). The proposed methodology is tried against distinctive pictures and it gives better PSNR that is Peak Signal-to-Noise Ratio.

Hybrid Approach for Noise Removal and Image Enhancement of Brain Tumors in Magnetic Resonance Images

In medical image processing, Magnetic Resonance Imaging (MRI) is one of significant diagnostic techniques. It provides high quality of important information about the analysis of human soft tissue when measured with CT imaging modalities; hence it is suitable for diagnosis at best. However, if it gives quality of information, image may distorted by noise because of image acquisition device and transmission. The noises in MR image reduces the quality of image and also damages the segmentation task which can lead to faulty diagnosis. Noises have to reduce at the same time there is no information loss. This paper propose a hybrid approach to enhance the brain tumor MRI images using combined features of Anisotropic Diffusion Filter (ADF) with Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF). ADF scheme provides a superior performance by removing noise while preserving image details and enhancing edges. MDBUTMF helps in image denoising as well as preserving edges satisfactorily when the noise level is high. The performance of this filter is evaluated by carrying out a qualitative comparison of this method with other filters namely, ADF filter, Modified Decision Algorithm, Median filter, MDBUTMF.

Removal of Impulse Noise using First Order Neighborhood Mean Filter

Noise removal is one of the most important parts to get original image from highly corrupted image. The image can be corrupted with noise during transmission from noisy channel, sensors or due to some environmental conditions. This makes the image visually unpleasant. Impulsive noise may occur during transmission which highly corrupts the image. In this paper an algorithm is designed to remove the impulsive noise (salt and pepper) from corrupted gray scale and color images. In past years researchers proposed many algorithms to remove the impulse noise but they fail to give better results at high noise density i.e. 80%-90%. The proposed algorithm works on two stages first stage is to detect the noisy pixel and the second stage is to replace the noisy pixel. This algorithm considers first order neighborhood pixels for detecting the noisy pixel and mean filter is used for de-noising. Color images are also de-noised by extracting the R, G and B pixels from noisy image and then they are denoised separately and then merged together again to form the color image. All the other algorithms are compared with the proposed algorithms and found that the proposed algorithm have good noise removal capabilities at high densities. The presented algorithm shows better results than Standard Median Filter (SMF), Adaptive Median Filter (AMF), Progressive Switched Median Filter (PSMF), Decision Based Algorithm (DBA), Modified Decision Based Algorithm (MDBA), Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF), and Modified Non-Linear Filter (MNF). Different grayscale and color images are tested by using the algorithm and it gave better Peak Signal Noise Ratio (PSNR) and Image Enhancement Factor (IEF) at low, medium and high noise densities.

An Improved Decision based Asymmetric Trimmed Median Filter for Removal of High Density Salt and Pepper Noise

improved decision based asymmetric trimmed median filter (IDBATMF) algorithm for the restoration of gray scale, and color images that are highly corrupted by salt and pepper noise (SPN) is proposed in this paper. The proposed algorithm selects a 3 × 3window around pixel being processed. If the selected window consists of 0's, 255's and other random values, then the noisy pixel is replaced with the trimmed median value of the elements in selected window. When all the pixel values are 0's and 255's then the decision is taken based on numbers of 0 and 255 values in the window. If 6 or more pixels are 0 (or 255) then the pixel being processed is replaced with 0 (or 255), else it is replaced with mean of value of all the elements present in the selected window. Setting the pixel as 0 or 255 ensures that a large black or white patch of the image is restored satisfactorily. The proposed algorithm shows better results than existing median filters including the recent Modified Decision Based Unsymmetrical Trimmed Median Filter (MDBUTMF). Experimental results show improvements both visually and quantitatively compared to that of the MDBUTMF and other standard filters.

Restoration of Images Corrupted by High Density Salt & Pepper Noise through Adaptive Median Based Modified Mean Filter

In this paper an efficient algorithm is proposed for removal of salt & pepper noise from digital images. Salt and pepper noise in images is present due to bit errors in transmission or introduced during the signal acquisition stage. It represents itself as randomly occurring white and black pixels. This noise can be removed using standard Median Filter (SMF), Progressive Switched Median Filter (PSMF) under low density noise conditions. Decision Based Algorithm (DBA) and Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) do not give better results at high noise density. So, in this project, this drawback will be overcome by using Adaptive Median based Modified Mean Filter (AMMF). This proposed algorithm shows better Peak Signal-to-Noise Ratio and clear image than the existing algorithm. Keywords- Median filter, Progressive Switched Median Filter, Decision Based Algorithm, Modified Decision Based Unsymmetric Trimmed Median Filter

A New and Efficient Method for Removal of High Density Salt and Pepper Noise Through Cascade Decision based Filtering Algorithm

In this paper, a new and efficient cascade decision based filtering algorithm for the removal of high density Salt and Pepper Noise in images is proposed. The proposed algorithm is a cascaded filter employing a Modified Decision Based Median Filter as its first stage of operation. The second stage involves a combination of a new algorithm that calculates mean of difference in neighborhood pixels and a Modified Unsymmetric Trimmed Mean Filter. The proposed algorithm when compared with existing non-linear filters such as Standard Median Filter (SMF), Adaptive Median Filter (AMF), Decision Based Algorithm (DBA), Progressive Switch Median Filter (PSMF), Modified Decision Based Algorithm (MDBA) and Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) produces a better Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF) even at high density salt and pepper noise. The proposed algorithm is tested for different images and the results are compared with other filters. The proposed algorithm results better in terms of image restoration and quantitative measures.