Noise removal using First Order Neighborhood Mean Filter

Restoration of original image corrupted with high density salt-and-pepper noise is still a challenging task. In this letter, we propose here a new method; Multi-Pass Unsymmetric Trimmed Median Filter (MPUTMF) [versions ‘a’ and ‘b’], to restore an image affected with high density salt-and-pepper noise, with better edge preservation. The MPUTMFa can do the restoration within two passes over the noisy image using the preprocessed pixels obtained in the same pass, where as MPUTMFb can take up to six passes over the noisy image for restoration without using the preprocessed pixels. MPUTMFb is computationally efficient on single core processor systems where as MPUTMFb is well suited to be implemented on parallel processing systems or GPUs to achieve higher computational efficiency. The proposed methods are compared with conventional as well as advanced algorithms like Median Filter (MF), Adaptive Median Filter (AMF), Efficient Decision Based Algorithm (EDBA), Improved Decision Based Algorithm (IDBA) and Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF). The experimental analysis (visual and quantitative) shows that our method gives better results on images affected with high density salt-and-pepper noise. Peak Signal-to-Noise Ratio (PSNR) and Image Enhancement Factor (IEF) are used for quantitatively evaluating the results of proposed algorithm(s).KeywordsImage restorationSalt-and-pepper noiseMedian filterTrimmed median filterImpulse noiseParallel processing

Spline-based approach is proposed to remove very high density salt-and-pepper noise in grayscale and color images. The algorithm consists of two stages, the first stage detects whether the pixel is noisy or noise-free. The second stage removes the noisy pixel by recursive spline interpolation filter. The proposed recursive spline interpolation filter is based on the neighborhood noise-free pixels and previous noise-free output pixel; hence, it is termed as recursive spline interpolation filter. The performance of the proposed algorithm is compared with the existing algorithms like standard median filter, decision-based filter, progressive switched median filter, and modified decision-based unsymmetric trimmed median filter at very high noise density. The proposed algorithm gives better peak signal-to-noise ratio, image enhancement factor, and correlation factor results than the existing algorithms.

Noise Suppression from images is one of the most important concerns in digital image processing. Impulsive noise may occur during image acquisition, transmission or storage Noise should be removed in such a way that important information of image should be preserved. We can use so many algorithms for getting the original image, by removing salt and pepper noise from the corrupted images. In this paper an algorithm is proposed for the restoration of gray scale images that are highly corrupted by impulse noise (salt and pepper noise). There are two phases in the proposed algorithm. First phase detects whether the processing pixel is corrupted or not. In the Second phase it recreates the corrupted pixel by means of the proposed algorithm. This algorithm shows better results than the Standard Median Filter (MF), Center Weighed Median Filter(CWM), Adaptive Median Filter(AMF), Adaptive Center Weighed Median Filter (ACWM), Decision Based Algorithm (DBA) and Modified Decision Based Unsymmetrical Trimmed Median Filter Algorithm(MDBUTMF). Obtained results with different grayscale images shows that proposed algorithm gives better Peak Signal-to-Noise Ratio (PSNR) and less Computational time and works well in removing salt and pepper noise at low, medium and high noise densities.

In the field of image processing, removal of noise from Gray scale as well as RGB images is an ambitious task. The important function of noise removal algorithm is to eliminate noise from a noisy image. The salt and pepper noise (SPN) is frequently arising into Gray scale and RGB images while capturing, acquiring and transmitting over the insecure several communication mechanisms. In past, the numerous noise removal methods have been introduced to extract the noise from images adulterated with SPN. The proposed work introduces the SPN removal algorithm for Gray scale at low along with high density noise (10\% to 90\%). According to the different conditions of proposed algorithm, the noisy pixel is reconstructed by Winsorized mean or mean value of all pixels except the centre pixel which are present in the processing window. The noise from an image can be removed by using the proposed algorithm without degrading the quality of image. The performance evaluation of proposed and modified decision based unsymmetric median filter (MDBUTMF) is done on the basis of different performance parameters such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), Image Enhancement Factor (IEF) and Structure Similarity Index Measurement (SSIM).

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.

Multiclass SVM based adaptive filter for removal of high density impulse noise from color images

In the world of image processing domain, removal of high density impulse noise is always a prominent area for research. In this paper, an efficient Modified Decision based unsymmetric trimmed median filter algorithms for the removal of impulse noise has been proposed with color images rather than gray scale images by separation red- green- blue plane of color image. The performance of the system is analyzed in terms of Mean square error (MSE), Peak signal to noise ratio (PSNR) image enhancement factor (IEF) and time required for executing the algorithms for different noise densities. Simulation results shows that proposed algorithm outperforms the existing algorithms even at high noise densities for color images. Many experiments are conducted to validate efficiency of the proposed algorithm.

There are many techniques for face recognition which compare the desired face image with a set of faces images stored in a database. Most of these techniques fail if faces images are exposed to high-density noise. Therefore, it is necessary to find a robust method to recognize the corrupted face image with a high density noise. In this work, face recognition algorithm was suggested by using the combination of de-noising filter and PCA. Many studies have shown that PCA has ability to solve the problem of noisy images and dimensionality reduction. However, in cases where faces images are exposed to high noise, the work of PCA in removing noise is useless, therefore adding a strong filter will help to improve the performance of recognizing faces in the case of existing high-density noise in faces images. In this paper, Median filter, Hybrid Median Filter, Adaptive Median filter, and Adaptive Weighted Mean Filter were used to remove the noise from the faces images, and they were compared in order to use the best of these filters as a pre-processing step before the face recognition process. Experimental results showed that the Adaptive Weighted Mean Filter gave better results compared with the other filters. Thus, the performance of face recognition process was improved under high-density noise using the Adaptive Weighted Mean Filter and Principal Component Analysis. For the corrupted images by 90 % noise density, Recognition rate by using Median Filter reached 0% and 33% by using Hybrid Median Filter. While Recognition rate by using the Adaptive Median Filter and Adaptive Weighted Mean Filter reached 100%.

This paper proposes a novel improved median filter algorithm for the images highly corrupted with salt-and-pepper noise. Firstly all the pixels are classified into signal pixels and noisy pixels by using the Max-Min noise detector. The noisy pixels are then separated into three classes, which are low-density, moderate-density, and high-density noises, based on the local statistic information. Finally the weighted 8-neighborhood similarity function filter, the 5×5 median filter and the 4-neighborhood mean filter are adopted to remove the noises for the low, moderate and high level cases, respectively. In experiment, the proposed algorithm is compared with three typical methods, named Standard Median filter, Extremum Median filter and Adaptive Median filter, respectively. The validation results show that the proposed algorithm has better performance for capabilities of noise removal, adaptivity, and detail preservation, especially effective for the cases when the images are extremely highly corrupted.

A new method based on adaptive median filtering is proposed in this paper to remove high density impulse noise. This method replaces central noisy pixel having gray scale value 0 or 255 by median value of 3×3 window. Algorithm is proposed to replace the noisy neighbor pixels in the window. The clean pixels are then used to calculate the median value, which then replaces noisy central pixel. Proposed method has been tested on images which were contaminated by impulse noise of different densities, and results are compared with existing simple median filter with window size of 3 × 3, 5 × 5, and adaptive median filter. Results shows that Proposed method gives better Mean Square Error (MSE), Universal Quality Index (UQI), and Peak Signal to Noise Ratio (PSNR), when compared with the simple and adaptive median filter.

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.

A new switching-based trimmed median filter to remove high-density salt-and-pepper noise in digital images is proposed. Initially, a 3×3 sliding window is applied on each pixel in the noisy image. The minimum- and maximum-intensity values are trimmed, and the noisy pixels are detected based on the predefined threshold value. In the filtering stage, the noisy pixels are replaced by median value of uncorrupted pixels in the trimmed array. At very high noise density, if all the pixels in the sliding window are corrupted, then the proposed algorithm replaces noisy pixels by the midpoint of recently processed pixels. The experimental results for various test images show that the performance of the proposed algorithm is superior to the existing algorithms, namely SMF, WMF, CWMF, AMF, DBA, and MDBUTMF in terms of visual quality and edge preservation, even at noise levels as high as 95%.

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.

Digital images, particularly medical images, are prone to various noise attacks, such as additive, multiplicative, etc. Salt and pepper noise (SPN) is a type of multiplicative noise that degrades image quality and complicates identification, potentially leading to inaccurate diagnoses. Removing SPN is crucial yet challenging as noise removal can also eliminate important edge details. This study addresses the challenge of removing SPN without losing edge details. Existing SPN filtering algorithms, such as Average Filter (AF), Median Filter (MF), Switched MF (SMF), Adaptive MF (AMF), and Decision-based MF (DMF), perform poorly under high SPN attacks. We propose two effective SPN filters, CA_SPN1 and CA_SPN2, under the framework of Cellular Automata (CA) and MF. CA_SPN1 excels at high SPN levels, while CA_SPN2, a combination of CA_SPN1 and AMF, is effective across all SPN levels. Quality evaluations using Image Quality Assessment (IQA) metrics – Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Figure of Merit (FOM), and Perceptual Similarity (PSIM) – demonstrate that the proposed algorithms restore images more effectively while preserving edge details compared to state-of-the-art algorithms. Furthermore, experimental results confirm the superior restoration quality of the proposed filters, particularly under high SPN conditions.

There has been extensive research on removing universal noise from corrupted images using various kinds of non-linear filters. Median and bilateral filters are basic nonlinear filters which have been modified over the years to improve the quality of images. Median filters are good at removing Impulse noise, while these filters fail terribly in removing Gaussian noise. For Gaussian noise removal bilateral filter is preferred and successful over the years. But bilateral filter is not effective in removing impulse noise. Switching Bilateral Filter (SBF) works good for removing universal noise which involves noise detection followed by filtering. Detection plays vital role in image denoising of highly corrupted images. Boundary Discriminated Noise Detection (BDND) algorithm is very efficient in detection of noise even in highly corrupted images. Boundary Discriminated Switching Bilateral Filter (BDSBF) is proposed which employs BDND detection followed by SBF for better accuracy in noise detection even at higher noise conditions. Proposed filter also changes the size of the window based on the noise density computed from noise statistics after the BDND detection. Proposed filter shows superior results over SBF under high noise densities and high noise standard deviations.