A new approach for SPN removal: nearest value based mean filter

To overcome the drawbacks of existing filters for salt and pepper noises, an adaptive probability filter is proposed. For an image, it detects salt and pepper noises based on the characteristic of minimum and maximum intensity values of the images, as well as the distribution of noise. If the noise‐free intensities in neighbourhood repeat with a certain probability, the noise‐free intensity with highest repeated frequency is used to remove noise based on the statistical significance; otherwise, the median of noise‐free pixels in neighbourhood is used to remove noise. Experiments show that the proposed method is capable of detecting noise more accurately and perform much better than the existing distinguished filters in terms of peak‐signal‐to‐noise ratio, image enhancement factor, and visual representation at all the 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).

In this paper, we propose a two-stage method for demising images heavily contaminated by salt and pepper noise. In the first stage, we use adaptive median filter to detect the noisy pixels. All the pixels are marked as noisy or noise-free pixels. In the second stage, we take the noisy pixels as inpainting regions and noise-free pixels as true information. The inpainting task is done by the normalized mean curvature flow. The method is generalized to color image. Experimental results show that the proposed algorithm has advantages over nonlinear filtering or regularizing methods in terms of edge preservation and noise removal and is competitive with other two-stage methods in the literature.

This paper presents the comparison of filtering methods for a contrast enhancement of computed tomography (CT) brain images. Each method consists of three filter consecutively which is a combination of the low order linear filter such as Gaussian filter, disk filter, average filter and median filter with an adaptive filter method and unsharp filter. The process starts with filtering the CT brain image using low order linear filter, then proceeds with adaptive averaging filter and ends with unsharp filter. In this paper, there are two criteria, peak signal to noise ratio and mean square error, that were adopted for performance assessment. Our preliminary results showed that the combination of Gaussian filter with adaptive filter and unsharp filter gives the good result in removing the noise and edge detection. This method improved the CT brain image and the gyri and sulci can be easily identified.

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.

The study introduces an adaptive switching interpolation filter (ASIF) for restoring images contaminated with salt & pepper impulse noise. The new method restores noisy pixels by applying a new interpolation scheme that combines Shepard's inverse distance weighting and Simpson's natural neighbour interpolation techniques. The new interpolation scheme is designed based on the percentage of the overlapping area and the distance between the sites created by noisy pixels with the sites of uncorrupted pixels in the Voronoi tessellation. Since natural neighbour‐based techniques do not support extrapolation, the proposed algorithm performs Shepard's inverse distance weighting‐based restoration when the noisy pixels fall outside the convex hull formed by uncorrupted pixels. As the proposed method combines the advantages of Simpson's natural neighbour and Shepard's inverse distance weighting interpolation methods, it better preserves the natural intensity variations in images and provides smoother approximation than other methods used in the comparative study. Visual and quantitative experimental analysis performed on various images with peak signal to noise ratio, mean structural similarity index measure, image enhancement factor, and feature‐similarity index measure demarcates the improved capability of ASIF over other comparative filters.

Image processing has an important role in signal processing. The occurrence of noise in the image can degrade its quality and can cause loss of information. In this paper, design of Gaussian, mean, and median filters is considered. Gaussian and mean filters are linear filters and are designed using convolution between 3 × 3 image pixel matrix and kernel matrix. The median filter is a nonlinear filter. An SRAM-based FPGA implementation of such a filter is susceptible to memory bit flips that are caused by single-event upsets (SEUs). Hence, a protection method is needed to ensure proper working of the median filter. Here, the median filter design proposed in (Aranda et al. in J. IEEE Transactions on Nuclear Science 64:2219–2226, 2017 [7]) is used to check if the median value obtained is within a dynamic range. The performance of these three filters is evaluated by considering image quality metrics such as PSNR and correlation coefficient with four different noises. The results show choice of the filter for denoising, based on the type of noise present in the image. In recent times, FPGA technology has become a reliable method for implementing image processing algorithms due to their fast response compared to image processing in MATLAB. The noisy image to pixel conversion is done in MATLAB. The 3 × 3 sliding window approach is used for filter coding in Verilog, and the FPGA synthesis on virtex5 is done with Xilinx ISE 14.5. The images are reconstructed with filtered pixels in MATLAB.

This study presents a new approach for recovering an image perturbed by salt and pepper noise (SPN) using a hybrid genetic algorithm (HGA) at all densities, called effective HGA (EHGA). The main contribution of the proposed algorithm is combining the genetic algorithm with image denoising methods that are integrated into the population to achieve rapid convergence. The idea is to evolve a group of individuals into a number of iterations using crossover and mutation operators. This approach evolves a set of images rather than a set of parameters from the filters. Experimental results of simulation on different images using peak signal‐to‐noise ratio, structural similarity index metric, image enhancement factor and Universal Quality Index show that the proposed algorithm outperforms other methods in removing the SPN qualitatively and quantitatively if the noise density is moderate and high. EHGA also preserves important features such as texture and corners of the image.

Laser image often mixes with salt and pepper noise when obtained and transmitted by image sensor. The salt and pepper noise not only makes the quality of laser image deteriorated but also causes the detail feature of laser image flooded which contains very important structure information. To remove salt and pepper noise effectively meanwhile ensure image details clear and completed, a Novel Adaptive Switching Median (NASM) filter based on local salt and pepper noise density is presented in this paper. Pixel points are divided into salt and pepper noise points and signal points according to two level detection mechanisms firstly, then, local salt and pepper noise density is introduced here to determined filter window of every noise point, only noise points are filtered by different size window adaptively whereas signal points are kept unprocessed finally. Through analyzing of experimental results, it shows that NASM filter can remove salt and pepper noise effectively meanwhile protect image details well thus getting better filtering performance compared with Traditional Median (TM) filter and other improved Switching Median (SM) filters for laser image.

A numbers of algorithms have been proposed for the removal of fixed valued impulse noise (salt and pepper) from highly corrupted gray scale and color images, but they failed to give better results at high noise densities. This paper is designed in accordance with the proposed algorithm to get better result at high noise density levels. The proposed algorithm works on two steps for de-noising the image first step is to detect that the pixel is noisy or not and the second step is the replacement of that noisy pixel. The proposed algorithm considers first order neighborhood pixels for detecting the noisy pixel and mean filter is considered. Color images are also de-noised by extracting the R, G and B pixels from noisy image and then they are de-noised separately and then merged together to again form the color image. Proposed algorithm is compared with all other standard and well known algorithms and found to have good noise removal capabilities at high densities. This algorithm shows better results than Median Filter (MF), 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.

A hypergraph-based algorithm for image restoration from salt and pepper noise

The myoelectric signal can be used to control many rehabilitation systems, for instance, prostheses and artificial neuromuscular electrical stimulation toward restoring movement to spinal cord injured subjects. These mobile systems are usually used in different environments and thus are being exposed to different noise levels with characteristics not completely known. In this article, three main techniques for noise reduction were evaluated: wavelet transform (WT), adaptive digital filters, and nonadaptive digital filters. The WT was used to reconstruct the signal with the components without noise information. Adaptive filters were designed using least mean square (LMS) and recursive least square (RLS) algorithms. Finite-impulse response (FIR) and infinite-impulse response (IIR) nonadaptive filters were used for comparison to both the adaptive filters and the signal reconstruction through the WT.

In this paper, three easily implemented hardware algorithms, including the adaptive prediction error filter based on the Gram-Schmidt algorithm (GS-APEF), the least mean square adaptive filter and the comb filter, are extensively investigated for artifact denoising on a constructed semi-simulated database with varied ten-fold frequency stimulation. By implementing the GS-APEF in the field-programmable gate array (FPGA) and using the edge noise mitigating technique, a stimulation artifact denoising system is designed to realize real-time stimulation artifact removal under varied ten-fold frequency functional electrical stimulation. Good performance of the artifact denoising is demonstrated in proof-of-concept experiments on able-bodied subjects with a mean correlation coefficient between the root mean square profile of denoised surface electromyography and volitional force of 0.94, verifying the validity of the proposed prototype.

Image quality metrics have become more and more popular in the image processing community. However, so far, no one has been able to define an image quality metric well correlated with the percept for overall image quality. One of the causes is that image quality is multi-dimensional and complex. One approach to bridge the gap between perceived and calculated image quality is to reduce the complexity of image quality, by breaking the overall quality into a set of quality attributes. In our research we have presented a set of quality attributes built on existing attributes from the literature. The six proposed quality attributes are: sharpness, color, lightness, artifacts, contrast, and physical. This set keeps the dimensionality to a minimum. An experiment validated the quality attributes as suitable for image quality evaluation. The process of applying image quality metrics to printed images is not straightforward, because image quality metrics require a digital input. A framework has been developed for this process, which includes scanning the print to get a digital copy, image registration, and the application of image quality metrics. With quality attributes for the evaluation of image quality and a framework for applying image quality metrics, a selection of suitable image quality metrics for the different quality attributes has been carried out. Each of the quality attributes has been investigated, and an experimental analysis carried out to find the most suitable image quality metrics for the given quality attributes. For the sharpness attributes the Structural SIMilarity index (SSIM) by Wang et al. (2004) is the the most suitable, and for the other attributes further evaluation is required.

Motion-induced artifact detection has become a fixture in the assessment of functional magnetic resonance imaging (fMRI) quality control. However, the effects of other MR image quality (IQ) metrics on intrinsic connectivity brain networks are largely unexplored. Accordingly, we report herein the initial assessment of the effects of a comprehensive list of IQ metrics on resting state networks using a multivariate analysis of covariance (MANCOVA) approach based on high-order spatial independent component analysis (ICA). Three categories of MR IQ metrics were considered: (1) metrics for artifacts including the AFNI outlier ratio and quality index, framewise displacement, and ghost to signal ratio, (2) metrics for the temporal quality of MRI data including the temporal framewise change in global BOLD signals (DVARS), global correlation of time-series, and temporal signal to noise ratio, (3) metrics for the structural quality of MRI data including the entropy focus criterion, foreground-background energy ratio, full-width half maximum smoothness, and static signal to noise ratio. After FDR-correction for multiple comparisons, results showed significant effects of the static and temporal signal to noise ratios on the spatial map intensities of the basal ganglia, default-mode and cerebellar networks. AFNI outlier ratio, framewise displacement and DVARS exhibited significant effects on the BOLD power spectra of sensorimotor networks. The global correlation of time-series displayed wide-spread modulation of the spectral power in most networks. Further investigations of the effect of IQ metrics on the characteristics of intrinsic connectivity brain networks allow more accurate interpretation of the fMRI results.