Salt and pepper noise reduction and edge detection algorithm based on neutrosophic logic

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.

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.

In this study, an analog circuit design instead of using a software is utilized to remove Salt and Pepper (S&P) noise in image. For this purpose, a digital image filtering method which is called median filter is carried out. In median filter method, a current-mode nine-input one-output circuit architecture is preferred since 3 × 3 filtering mask is employed. Median filter circuit is based on a modified bitonic sort network and consists of 26 max/min selector circuits. A current-mode two-input two-output circuit is designed as a max/min selector and there is no biasing current source in the max/min selector structure. In analysis part, two 150 × 150 images with different amount of S&P noise are used and the noise in image is successfully reduced with the help of the proposed circuit design. The performance of the suggested median filter is also verified through the quality measurement parameters such as Structural Similarity Index Metric, Mean Squared Error, Mean Absolute Error and Peak Signal to Noise Ratio. TSMC CMOS 0.18 µm process model is utilized to simulate the introduced median filter circuit.

In this article, spatial-domain filtering algorithms were developed to suppress additive noise in magnetic resonance (MR) imaging. It is difficult to suppress MR image noise because it corrupts almost all pixels in an image. The purpose of noise reduction is to curb the noise with high efficiency while keeping the edges and other detailed features as much as possible. The present article focused on developing quite efficient noise reduction by using an edge detection technique and hybrid mean Lee filters to suppress MR image noise quite effectively in spatial domain without yielding much distortion and blurring. The performances of the developed filter were compared with the existing filters in terms of universal quality index, method noise, and execution time. Among all existing filters, the edge detection technique and hybrid mean Lee filter was found to be best for suppressing MR image noise.

Edge Detection is a very essential part in digital image processing. In case of applications which require object information or feature extraction in an image, edge detection can play a vital role. Edge occurs where there is discontinuity or a sharp change in the intensity function. Now there are many operators for edge detection, but the challenge is to get better results from the existing system. This paper represents a hybrid approach which combines the Sobel and Canny edge detectors. It also uses a median filter to remove the salt and pepper noise from the image which consequently smoothen the image and edges can be detected easily. A comparison has also been shown between the hybrid approach with median filter and without median filter. The comparative study shows that using median filter shows enhanced result. As it filters the noise from the image, the accuracy of edge detection improved and achieved ideal effect.

To preserve the edges from corrupted noise, median filter is a popular choice for filtering algorithms. The salt and pepper noise is one of the main problems present while capturing an image. To solve this problem the edges were detected, using different edge detection operators which include Roberts, Sobel, Prewitt and Canny, prior to processing by a median filter. Several simulations were carried out over a number of digital images. The peak signal to noise ratio (PSNR) is higher using Prewitt operator than Sobel operator. Similarly, results also confirm the better performance of Canny edge detection algorithm over others for edge detection however computational cost is high in it compared to other methods.

7 - A survey on neutrosophic medical image segmentation

Background:Common carotid artery (CCA) ultrasound with measurement of intima-media thickness (IMT) is a safe and noninvasive technique for assessing subclinical atherosclerosis and determining cardiovascular risks. Moreover, the pattern of wall thickening in the brachial artery (BA) is rather diffuse compared to the carotid artery and may be a more sensitive indicator of long-term systemic exposure to risk factors. Therefore noninvasive evaluation of mechanical parameters changes of both arteries has gained the attention of researchers.Objectives:The aim of this study was to compare different edge detection techniques with speckle reducing anisotropic diffusion (SRAD) de-noising filter in ultrasound images of both arteries.Patients and Methods:In a cross-sectional design, an examination was performed on ten men with mean age of 40 ± 5 years from September 2012 to March 2013 through random sampling. An ultrasonic examination of the left CCA and BA was performed. The program was designed in the MATLAB software to extract consecutive images in JPEG format from the AVI. Another program was designed in the MATLAB software to apply regions of interest (ROI) on the IMT of the posterior wall of common carotid and brachial arteries. Next, different edge detections and SRAD filter were applied to the ROI, separately. Finally, the program measured mean-squared error (MSE) and peak signal to noise ratio (PSNR).Results:The lowest values of MSE and highest values of PSNR were achieved by Canny edge detection with de-noising SRAD filter for IMT of left CCA and BA in 90 frames.Conclusions:Based on the result, by measuring the MSE and PSNR, this study showed Canny edge detection with SRAD filter is better than other edge detections in terms of speckle suppression and details preservation in CCA and BA ultrasound images.

The essential constraint on the input images to any computer vision technology is its quality. Acquiring noise free digital images is a challenge as it depends on several factors. Developing algorithms to remove noise is one way to improve the image quality. Salt and pepper noise degrades the image. The challenge here is to restore the lost information without distorting the edges. This paper introduces a new algorithm that reduces high density salt and pepper noise from images. Restoration is done by calculating the weighted mean of the nearby pixels. Weights are assigned unsymmetrically to pre-processed and unprocessed pixels. The quality was judged based on the PSNR value. The algorithm restores information for highly corrupted images. Salt and pepper noise are usually filtered with variants of the median filter. This paper provides an alternate way for noise reduction.

Edge detection has beneficial applications in the fields such as machine vision, pattern recognition and biomedical imaging etc. Edge detection highlights high frequency components in the image. Edge detection is a challenging task. It becomes more arduous when it comes to noisy images. This study focuses on fuzzy logic based edge detection in smooth and noisy clinical images. The proposed method (in noisy images) employs a 3×3 mask guided by fuzzy rule set. Moreover, in case of smooth clinical images, an extra mask of contrast adjustment is integrated with edge detection mask to intensify the smooth images. The developed method was tested on noise-free, smooth and noisy images. The results were compared with other established edge detection techniques like Sobel, Prewitt, Laplacian of Gaussian (LOG), Roberts and Canny. When the developed edge detection technique was applied to a smooth clinical image of size 270×290 pixels having 24 dB ‘salt and pepper’ noise, it detected very few (22) false edge pixels, compared to Sobel (1931), Prewitt (2741), LOG (3102), Roberts (1451) and Canny (1045) false edge pixels. Therefore it is evident that the developed method offers improved solution to the edge detection problem in smooth and noisy clinical images.

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.

Automatic image segmentation turns severely crucial for the detection of tumor in the processing of medical image. Semi-automatic and manual techniques of segmentation require immense knowledge and time. These drawbacks however have been overcome by the hybrid segmentation, yet there are requirements to develop many techniques that are appropriate for the medical image segmentation. Hence, we generated image segmentation based on hybrid approach making use of the features of threshold segmentation technique and region growing techniques combined. This is followed by the pre-processing stage to give away an accurate extraction of brain tumor with the help of (MRI) magnetic resonance imaging. The threshold segmentation method is made use of for the segmentation. Four types of noise are undergone in the segmented image: Gaussian noise, salt and pepper noise, Poisson noise and speckle noise which are influenced in brain smear image and they are removed using the filters of four types: median filter, mean filter, wiener filter and Gaussian filter to test the efficiency of the variety of filters over the various kinds of noise. To estimate the parametric values, we can make use of PSNR, NAE, MSE and NK.

Early diabetes diagnosis allows patients to begin treatment on time, reducing or eliminating the risk of serious consequences. In this paper, we propose the Neutrosophic-Adaptive Neuro-Fuzzy Inference System (N-ANFIS) for the classification of diabetes. It is an extension of the generic ANFIS model. Neutrosophic logic is capable of handling the uncertain and imprecise information of the traditional fuzzy set. The suggested method begins with the conversion of crisp values to neutrosophic sets using a trapezoidal and triangular neutrosophic membership function. These values are fed into an inferential system, which compares the most impacted value to a diagnosis. The result demonstrates that the suggested model has successfully dealt with vague information. For practical implementation, a single-value neutrosophic number has been used; it is a special case of the neutrosophic set. To highlight the promising potential of the suggested technique, an experimental investigation of the well-known Pima Indian diabetes dataset is presented. The results of our trials show that the proposed technique attained a high degree of accuracy and produced a generic model capable of effectively classifying previously unknown data. It can also surpass some of the most advanced classification algorithms based on machine learning and fuzzy systems.

Outlier detection in neutrosophic sets by using rough entropy based weighted density method

In many cases it is required to evaluate the multiplicative noise relative variance in radar images, in particular, those formed by mm-band side looking aperture radars (SLARs). It is evident that. the more information about noise properties one has, the more effective the algorithms which can be designed for noise reduction in images. This evaluation, for example, is needed before image filtering by local statistical Lee or sigma filters or edge detection. These algorithms need for their normal operation the value of relative variance /spl sigma//sub /spl mu///sup 2/, which is not always known, and therefore, should be estimated. It is often desirable to perform this operation automatically. It is of great importance to derive an appropriate model describing the real-life situation for a certain application.