Showing papers on "Median filter published in 2021"

A Fast Defogging Image Recognition Algorithm Based on Bilateral Hybrid Filtering

Abstract: With the rapid advancement of video and image processing technologies in the Internet of Things, it is urgent to address the issues in real-time performance, clarity, and reliability of image recognition technology for a monitoring system in foggy weather conditions. In this work, a fast defogging image recognition algorithm is proposed based on bilateral hybrid filtering. First, the mathematical model based on bilateral hybrid filtering is established. The dark channel is used for filtering and denoising the defogging image. Next, a bilateral hybrid filtering method is proposed by using a combination of guided filtering and median filtering, as it can effectively improve the robustness and transmittance of defogging images. On this basis, the proposed algorithm dramatically decreases the computation complexity of defogging image recognition and reduces the image execution time. Experimental results show that the defogging effect and speed are promising, with the image recognition rate reaching to 98.8% after defogging.

Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network

Abstract: The objective of this study is to frame mammogram breast detection model using the optimized hybrid classifier. Image pre-processing, tumor segmentation, feature extraction, and detection are the functional phases of the proposed breast cancer detection. A median filter eliminates the noise of the input mammogram. Further, the optimized region growing segmentation is carried out for segmenting the tumor from the image and the optimized region growing depends on a hybrid meta-heuristic algorithm termed as firefly updated chicken based CSO (FC-CSO). To the next of tumor segmentation, feature extraction is done, which intends to extract the features like grey level co-occurrence matrix (GLCM), and gray level run-length matrix (GRLM). The two deep learning architectures termed as convolutional neural network (CNN), and recurrent neural network (RNN). Moreover, both GLCM and GLRM are considered as input to RNN, and the tumor segmented binary image is considered as input to CNN. The result of this study shows that the AND operation of two classifier output will tend to yield the overall diagnostic accuracy, which outperforms the conventional models.

A Novel Underwater Acoustic Signal Denoising Algorithm for Gaussian/Non-Gaussian Impulsive Noise

Abstract: Gaussian/non-Gaussian impulsive noises in underwater acoustic (UWA) channel seriously impact the quality of underwater acoustic communication. The common denoising algorithms are based on Gaussian noise model and are difficult to apply to the coexistence of Gaussian/non-Gaussian impulsive noises. Therefore, a new UWA noise model is described in this paper by combining the symmetric $\alpha$ -stable (S $\alpha$ S) distribution and normal distribution. Furthermore, a novel underwater acoustic signal denoising algorithm called AWMF+GDES is proposed. First, the non-Gaussian impulsive noise is adaptively suppressed by the adaptive window median filter (AWMF). Second, an enhanced wavelet threshold optimization algorithm with a new threshold function is proposed to suppress the Gaussian noise. The optimal threshold parameters are obtained based on good point set and dynamic elite group guidance combined simulated annealing selection artificial bee colony (GDES-ABC) algorithm. The numerical simulations demonstrate that the convergence speed and the convergence precision of the proposed GDES-ABC algorithm can be increased by 25% $\sim$ 66% and 21% $\sim$ 73%, respectively, compared with the existing algorithms. Finally, the experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that both the proposed wavelet threshold optimization method based on GDES-ABC and the AWMF+GDES algorithm can obtain higher output signal-to-noise ratio (SNR), noise suppression ratio (NSR), and smaller root mean square error (RMSE) compared with the other algorithms.

Texture defect classification with multiple pooling and filter ensemble based on deep neural network

Abstract: Fabric quality control is one of the most important phases of production in order to ensure high-quality standards in the fabric production sector. For this reason, the development of successful automatic quality control systems has been a very important research subject. In this study, we propose a Multiple Pooling and Filter approach based on a Deep Neural Network (MPF-DNN) for the classification of texture defects. This model consists of three basic stages: preprocessing, feature extraction, and classification. In the preprocessing stage, the texture images were first divided into n × n equal parts. Then, median filtering and pooling processes were applied to each piece prior to performing image merging. In the proposed pre-treatment stage, it is aimed to clarify fabric errors and increase performance. For the feature extraction stage, deep features were extracted from the texture images using the pretrained ResNet101 model based on the transfer learning approach. Finally, classification and testing procedures were conducted on the obtained deep-effective properties using the SVM method. The multiclass TILDA dataset was used in order to test the proposed model. In experimental work, the MPF-DNN model for all four classes achieved a significant overall accuracy score of 95.82%. In the results obtained from extensive experimental studies, it was observed that the proposed MPF-DNN model was more successful than previous studies that used pretrained deep architectures.

Fault Diagnosis of Steel Wire Ropes Based on Magnetic Flux Leakage Imaging Under Strong Shaking and Strand Noises

Abstract: Because of its flexibility, high strength, and durability, steel wire rope (SWR) is widely used in irrigation works, bridges, harbors, tourism, and many industrial fields as a vital component. Thus, it can cause accidents and economic losses if local flaws (LFs) of the SWR in service are not detected in time. This article points out two major problems in magnetic flux leakage (MFL) imaging-based nondestructive testing for fault diagnosis of SWR and proposes an integrated signal-processing method specifically designed for addressing the two problems. In this article, the MFL signals are collected by a detector that is formed by a set of permanent magnets and a Hall sensor array. Based on these multichannel MFL signals obtained from the Hall sensor array, we use the principle of multichannel signal fusion to determine rich information from all MFL signals. We solve the strand noise problem by an oblique-directional resampling and filtering method, which avoids severe attenuation in the LF signal. Moreover, the shaking noise is effectively removed by the proposed antishaking filtering based on the median filter. According to our simulation and experiment, the proposed fault diagnosis method for SWR significantly improves the performance of LF detection and localization under strong shaking and strand noises.

MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier

Abstract: Brain tumor characterizes the aggregation of abnormal cells in specific tissues of the brain zone. The prior distinguishing proof of brain tumors has a huge influence on the treatment and recovery of the patient. The identification of a brain tumor and its evaluation is commonly a troublesome and tedious assignment. For effective classification and grading of brain tumor images, in this paper, we present an automatic MRI brain tumor classification system. The proposed work consists of four modules namely, pre-processing, feature extraction, classification, and segmentation. Initially, the noise present in the input image is removed using the Median Filter because the noises present in the input images will affect the accuracy of the classification process. At once, the images are converted into 3 × 3 blocks. Then, the texture features are extracted from the pre-processed image. After the feature extraction process, the features are given to the adaptive k-nearest neighbor classifier to classify an image as normal or abnormal. Later, the tumor regions are segmented with the help of the optimal possibilistic fuzzy C-means clustering algorithm. Both classification and the segmentation appearance technique are evaluated in terms of accuracy; sensitivity as well as specificity. For experimental analysis two dataset are utilized namely, BRATS MICCAI brain tumor dataset and publically available dataset.

Feature Extraction-based Medical Image Watermarking Using Fuzzy-based Median Filter

Abstract: In recent days, securing the clinical images with high-level attacks is still a great challenge. The proposed algorithm focuses on developing an algorithm, which starts with pre-processing clinical...

Removal of high density salt and pepper noise in color image through modified cascaded filter

Abstract: A modified cascaded filter (MCF) for the restoration of color pictures that are extremely corrupted by salt and pepper noise and random valued impulse noise are projected in this article. MCF algorithm restores the noisy pixel by trimmed median value while other pixel values, 0’s and 255’s are present in the selected window using decision based median filter (DMF) and when the pixel values are 0’s and 255’s then the noise pixel is replaced by mean value of all the elements present in the selected window using unsymmetrical trimmed mean filtering. This modified cascaded filter proves better results than the standard median filter, DMF, and alpha trimmed median filter, UTMF. The MCF is analyzed against various color images and it provides superior peak signal-to-noise ratio and image enhancement factor.

Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model.

Abstract: The process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.

An efficient approach for brain tumor detection and segmentation in MR brain images using random forest classifier

Abstract: Nowadays, the most demanding and time consuming task in medical image processing is Brain tumor segmentation and detection. Magnetic Resonance Imaging (MRI) is employed for creating a picture of an...

Time-Varying Graph Signal Denoising via Median Filters

Abstract: Graph Signal Processing (GSP) leverages pair-wise relationship between nodes of a graph to formulate operators on signals defined over the nodes. Most existing graph signal operators in the literature are linear, and can be described by linear transformation matrices. Recently, works are emerging that consider the time correlation of graph signals, leading to time-vertex signal processing. By exploiting the joint correlations across the graph topology and time, better results can be obtained. In this brief, we propose a median operator that will leverage the joint correlation, for denoising time-varying graph signals. The median operator, known for its robustness to outliers in statistics, has been very successful in traditional signal processing, especially for images. The efficient and highly localised graph median filters developed here are applied to denoising real world sensor network data. Real world sensor nodes are usually resource limited in terms of their computational and communication capacity, hence the imperative requirement for efficient localised filters. Comprehensive experimental results will show that in some cases, the performance is significantly better than the equivalent linear operator counterpart.

Early detection of breast cancer using hybrid of series network and VGG-16

Abstract: Breast cancer is nowadays becoming a serious problem and acts as a main reason for death of women around the world. Hence various devices are being utilized for the detection of breast cancer at an earlier stage and diagnosing it in an earlier stage might even results in complete cure of the disease. Among the wide range of devices available, mammogram is one of the commonly employed and most effective approaches involved in the detection of breast cancer. It records the affected area in the form of mammogram images and these images are processed through image processing techniques for the detection of cancer affected regions. In this paper, novelties have given in all the image processing aspects such as filtering, segmentation, feature extraction and classification. The salt and pepper noises in the mammogram images are eliminated by the usage of novel decision based partial median filter. Then the filtered images are segmented based utilizing a novel technique which is formed on integrating the deep learning techniques of VGG-16 and series network. Features of the segmented images have extracted through BAT-SURF feature extraction, where the orientation of the interest points are extracted using Bat optimization algorithm along with SURF (i.e.) Speeded up Robust Features. It extract most important key points from SURF features and then the extracted image has classified by using the novel Gradient descent decision tree classifier in which a stable learning path provided for easy convergence. Then the performance of the proposed system has analyzed based on the performance metrics like accuracy, specificity, sensitivity, recall, precision, Jaccard coefficient, F score and missed classification. Based on the results obtained, the conclusion of the proposed work has attained enhanced results on comparing with other state of the art approaches. The accuracy value of the proposed hybrid VGG-16 and series network segmentation technique determined as 96.45 and similarly the accuracy value of the proposed Gradient Descent Decision Tree Classification technique has value shows 95.15.

Segmentation and classification of brain tumors using modified median noise filter and deep learning approaches

Abstract: The most vital challenge for a radiologist is locating the brain tumors in the earlier stage. As the brain tumor grows rapidly, doubling its actual size in about twenty-five days. If not dealt properly, the affected person’s survival rate usually does no longer exceed half a year. This can rapidly cause dying. For this reason, an automatic system is desirable for locating brain tumors at the early stage. In general, when compared to computed tomography (CT), magnetic resonance image (MRI) scans are used for detecting the diagnosis of cancerous and noncancerous tumors. However, while MRI scans acquisition, there is a chance of appearing noise such as speckle noise, salt & pepper noise and Gaussian noise. It may degrade classification performance. Hence, a new noise removal algorithm is proposed, namely the modified iterative grouping median filter. Further, Maximum likelihood estimation-based kernel principal component analysis is proposed for feature extraction. A deep learning-based VGG16 architecture has been utilized for segmentation purposes. Experimental results have shown that the proposed algorithm outperforms the well-known techniques in terms of both qualitative and quantitative evaluation.

Industrial Laser Welding Defect Detection and Image Defect Recognition Based on Deep Learning Model Developed

Abstract: Aiming at the problem of the poor robustness of existing methods to deal with diverse industrial weld image data, we collected a series of asymmetric laser weld images in the largest laser equipment workshop in Asia, and studied these data based on an industrial image processing algorithm and deep learning algorithm. The median filter was used to remove the noises in weld images. The image enhancement technique was adopted to increase the image contrast in different areas. The deep convolutional neural network (CNN) was employed for feature extraction; the activation function and the adaptive pooling approach were improved. Transfer Learning (TL) was introduced for defect detection and image classification on the dataset. Finally, a deep learning-based model was constructed for weld defect detection and image recognition. Specific instance datasets verified the model’s performance. The results demonstrate that this model can accurately identify weld defects and eliminate the complexity of manually extracting features, reaching a recognition accuracy of 98.75%. Hence, the reliability and automation of detection and recognition are improved significantly. The research results can provide a theoretical and practical reference for the defect detection of sheet metal laser welding and the development of the industrial laser manufacturing industry.

Analog circuit implementation based on median filter for salt and pepper noise reduction in image

Abstract: 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.

Structure-Oriented DTGV Regularization for Random Noise Attenuation in Seismic Data

Abstract: Noise attenuation is a very important step in seismic data processing, which facilitates accurate geologic interpretation Random noise is one of the main factors that lead to reductions in the signal-to-noise ratio (SNR) of seismic data It is necessary for seismic data, including complex geological structures, to develop a number of new noise attenuation technologies In this article, we concern with a new variational regularization method for random noise attenuation of seismic data Considering that seismic reflection events often have spatially varying directions, we first employ the gradient structure tensor (GST) to estimate the spatially varying dips point by point and propose the structure-oriented directional total generalized variation (DTGV) (SODTGV) functional Then, we employ the SODTGV as a regularizer to establish an $\ell _{2}$ -SODTGV model and develop the primal-dual algorithm for solving this model Next, the choice of the model parameters is discussed Finally, the proposed model is applied to restore noisy synthetic and field data to verify the effectiveness of the proposed workflow For contrastive methods, we select the structure adaptive median filtering (SAMF), anisotropic total variation (ATV), total generalized variation (TGV), DTGV, median filtering, KL transform, SVD transform, and curvelet transform The synthetic and real seismic data examples indicate that our proposed method can preferably improve the vertical resolution of seismic profiles, enhance the lateral continuity of reflection events, and preserve local geologic features while improving the SNR Moreover, the proposed regularization method can also be applied to other inverse problems, such as image processing, medical imaging, and remote sensing

A Comparative Analysis of Image Denoising Problem: Noise Models, Denoising Filters and Applications

Abstract: Noise reduction is a perplexing undertaking for the researchers in digital image processing and has a wide range of applications in automation, IoT (Internet of Things), medicine, etc. Noise generates maximum critical disturbances as well as touches the medical images quality, ultrasound images in the field of biomedical imaging. The image is normally considered as a gathering of data and the existence of noises degradation the image quality. It ought to be vital to reestablish the original image noises for accomplishing maximum data from images. Digital images are debased through noise through its transmission and procurement. Noisy image reduces the image contrast, edges, textures, object details, and resolution, thereby decreasing the performance of postprocessing algorithms. This paper mainly focuses on Gaussian noise, salt and pepper noise, uniform noise, speckle noise. Different filtering techniques can be adapted for noise declining to improve the visual quality as well as a reorganization of images. Here four types of noises have been undertaken and applied to process images. Besides linear and nonlinear filtering methods like Gaussian filter, median filter, mean filter and Weiner filter applied for noise reduction as well as estimate the performance of filter through the parameters like mean square error (MSE), peak signal to noise ratio (PSNR), average difference value (AD) and maximum difference value (MD) to diminish the noises without corrupting the medical image data.

Application of Denoising CNN for Noise Suppression and Weak Signal Extraction of Lunar Penetrating Radar Data

Abstract: As one of the main payloads mounted on the Yutu-2 rover of Chang’E-4 probe, lunar penetrating radar (LPR) aims to map the subsurface structure in the Von Karman crater. The field LPR data are generally masked by clutters and noises of large quantities. To solve the noise interference, dozens of filtering methods have been applied to LPR data. However, these methods have their limitations, so noise suppression is still a tough issue worth studying. In this article, the denoising convolutional neural network (CNN) framework is applied to the noise suppression and weak signal extraction of 500 MHz LPR data. The results verify that the low-frequency clutters embedded in the LPR data mainly came from the instrument system of the Yutu rover. Besides, compared with the classic band-pass filter and the mean filter, the CNN filter has better performance when dealing with noise interference and weak signal extraction; compared with Kirchhoff migration, it can provide original high-quality radargram with diffraction information. Based on the high-quality radargram provided by the CNN filter, the subsurface sandwich structure is revealed and the weak signals from three sub-layers within the paleo-regolith are extracted.

A novel multiscale transform decomposition based multi-focus image fusion framework

Abstract: In this work, we propose a novel multiscale transform decomposition model for multi-focus image fusion to get a better fused performance. The motivation of the proposed fusion framework is to make full use of the decomposition characteristics of multiscale transform. The nonsubsampled contourlet transform (NSCT) is firstly used to decompose the source multi-focus images into low-frequency (LF) and several high-frequency (HF) bands to separate out the two basic characteristics of source images, i.e., principal information and edge details. The common “average” and “max-absolute” fusion rules are performed on low- and high-frequency components, respectively, and a basic fusion image is generated. Then the difference images between the basic fused image and the source images are calculated, and the energy of the gradient (EOG) of difference images are utilized to refine the basic fused image by integrating average filter and median filter. Visual and quantitative using fusion metrics like VIFF, QS, MI, QAB/F, SD, QPC and running time comparisons to state-of-the-art algorithms demonstrate the out-performance of the proposed fusion technique.

An Efficient Classification of MRI Brain Images

Abstract: The unprecedented improvements in computing capabilities and the introduction of advanced techniques for the analysis, interpretation, processing, and visualization of images have greatly diversified the domain of medical sciences and resulted in the field of medical imaging. The Magnetic Resonance Imaging (MRI), an advanced imaging technique, is capable of producing high quality images of the human body including the brain for diagnosis purposes. This paper proposes a simple but efficient solution for the classification of MRI brain images into normal, and abnormal images containing disorders and injuries. It uses images with brain tumor, acute stroke and alzheimer, besides normal images, from the public dataset developed by harvard medical school, for evaluation purposes. The proposed model is a four step process, in which the steps are named: 1). Pre-processing, 2). Features Extraction, 3). Features Reduction, and 4). Classification. Median filter, being one of the best algorithms, is used for the removal of noise such as salt and pepper, and unwanted components such as scalp and skull, in the pre-processing step. During this stage, the images are converted from gray scale to colored images for further processing. In second step, it uses Discrete Wavelet Transform (DWT) technique to extract different features from the images. In third stage, Color Moments (CMs) are used to reduce the number of features and get an optimal set of characteristics. Images with the optimal set of features are passed to different classifiers for the classification of images. The Feed Forward - ANN (FF-ANN), an individual classifier, which was given a 65% to 35% split ratio for training and testing, and hybrid classifiers called: Random Subspace with Random Forest (RSwithRF) and Random Subspace with Bayesian Network (RSwithBN), which used 10-Fold cross validation technique, resulted in 95.83%, 97.14% and 95.71% accurate classification, in corresponding order. These promising results show that the proposed method is robust and efficient, in comparison with, existing classification methods in terms of accuracy with smaller number of optimal features.

Robust and imperceptible image watermarking technique based on SVD, DCT, BEMD and PSO in wavelet domain

Abstract: This article introduces a robust image watermarking primarily founded on DWT (discrete wavelet transform), BEMD (bi-dimensional empirical mode decomposition), DCT (discrete cosine transform), PSO (particle swarm optimization), and SVD (singular value decomposition). During the process of embedding, 2nd level DWT is used to decompose the cover image into sub-bands. DWT is also used for the decomposition of images for watermarking. Further, BEMD decomposition runs to implement on the selected band of DWT. For optimization, PSO is used for complex and multidimensional searches. Further DCT as well as SVD applied to the selected band. The embedding and scaling factors are embedded with the help of a security key. Further, this method is followed by using the inverse of ISVD, IDCT, IDWT, and IBEMD. The Watermark image is extracted by the extraction process. Experimental results show that the suggested technique is robust compared to several geometrical and non-geometrical attacks. Therefore, this proposed technique provides better visible quality and robustness against numerous attacks like salt and pepper, Gaussian filter, rotation, median filter, speckle, gamma correction, scaling, and shearing for gray scale images and provides the watermarked image with good quality.

Entropy based spatial domain image watermarking and its performance analysis

Abstract: Digital image watermarking technique based on LSB Substitution and Hill Cipher is presented and examined in this paper. For better imperceptibility watermark is inserted in the spatial domain. Further the watermark is implanted in the Cover Image block having the highest entropy value. To improve the security of the watermark hill cipher encryption is used. Both subjective and objective image quality assessment technique has been used to evaluate the imperceptibility of the proposed scheme.Further, the perceptual perfection of the watermarked pictures accomplished in the proposed framework has been contrasted and some state-of-art watermarking strategies. Test results demonstrates that the displayed method is robust against different image processing attacks like Salt and Peppers, Gaussian filter attack, Median filter attacks, etc.

White light three-dimensional imaging using a quasi-random lens.

Abstract: Coded aperture imaging (CAI) technology is a rapidly evolving indirect imaging method with extraordinary potential. In recent years, CAI based on chaotic optical waves have been shown to exhibit multidimensional, multispectral, and multimodal imaging capabilities with a signal to noise ratio approaching the range of lens based direct imagers. However, most of the earlier studies used only narrow band illumination. In this study, CAI based on chaotic optical waves is investigated for white light illumination. A numerical study was carried out using scalar diffraction formulation and correlation optics and the lateral and axial resolving power for different spectral width were compared. A binary diffractive quasi-random lens was fabricated using electron beam lithography and the lateral and axial point spread holograms are recorded for white light. Three-dimensional imaging was demonstrated using thick objects consisting of two planes. An integrated sequence of signal processing tools such as non-linear filter, low-pass filter, median filter and correlation filter were applied to reconstruct images with an improved signal to noise ratio. A denoising deep learning neural network (DLNN) was trained using synthetic noisy images generated by the convolution of recorded point spread functions with the virtual object functions under a wide range of aberrations and noises. The trained DLNN was found to reduce further the reconstruction noises.

A brain tumor image segmentation technique in image processing using ICA-LDA algorithm with ARHE model

Abstract: In digital image processing, image segmentation is the key methodology which is to be used frequently. In digital image processing, noise reduction and enhancement techniques are plays as a vital role. Brain is major and major organ of the human body which is to be controlled by the nervous system. In this paper, we proposed a brain tumor image enhancement technique with the help of the ICA-LDA (independent component analysis-linear discriminate analysis algorithm with ARHE (adaptive region based histogram enhancement) model. Image fusion technique is to apply for combination of the two or more input image. In this paper, the weighted average technique is to be used for image fusion techniques. The noise reduction and enhancement techniques are to be applied in preprocessing stage. The adaptive median filter is to be used for preprocessing stage. The ARHE (adaptive region based histogram enhancement) model is to be used for enhancement present in the preprocessing stage. The feature extraction and the feature optimization have to be utilized with the ICA (independent component analysis). The LDA (linear discriminate analysis) is to be used for the classification techniques. Using this classifier which is to separate the abnormal and normal stages. When the brain tumor is denoted as abnormal case then the morphological based segmentation is to be done. The simulation and result shows the analysis of various parameters such as specificity, sensitivity, positive predictive value, negative predictive value, accuracy, precision, and recall.

A novel robust zero-watermarking algorithm for medical images

Abstract: A novel robust zero-watermarking algorithm for medical images is presented in this paper. The multi-scale decomposition of bi-dimensional empirical mode decomposition (BEMD) has exhibited many attractive properties that enable the proposed algorithm to robustly detect the tampering regions and protect the copyright of medical images simultaneously. Given a medical image, we first decompose a medical image adaptively into a finite number of intrinsic mode functions (IMFs) and a residue, by taking a full advantage of BEMD. The first IMF starts with the finest scale retaining fragile information and is best suitable for tampering detection, while the residue includes robust information at the coarser scale and is applied to the protection of intellectual property rights of medical images. Next, the feature matrices are extracted from the first IMF and the residue via singular value decomposition, which achieves robust performance subject to most attacks. For a given watermark image, it is encrypted by Arnold transform to enhance the security of the watermark. Then, the feature images are constructed by performing the exclusive-or operation between the encrypted watermark image and the extracted feature matrices. Finally, the feature images are securely stored in the copyright authentication database to be further used for copyright authentication and tampering detection. A large number of experimental results and comparisons with existing watermarking algorithms confirm that the newly proposed watermarking algorithm not only has strong ability on tampering detection, but also has better performance in combating various attacks, including cropping, Gaussian noise, median filtering, image enhancement attacks, etc. The newly developed algorithm also shows great promise in processing natural images.

Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

Abstract: In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.

High Accuracy and Real-Time Positioning Using MODWT for Long Range Asymmetric Interferometer Vibration Sensors

Abstract: The key technology to demodulate the vibration position along the sensing fiber link in the asymmetric fiber interferometer based vibration sensors is to extract similar variation features with high efficiency, so that the asymmetry of the vibration sensors can be effectively eliminated. The wavelet-based approaches have been developed and demonstrated as effective methods for the time-frequency feature analysis of non-stable signals. In this article, an ameliorated real-time positioning algorithm using maximal overlap discrete wavelet transformation (MODWT) for the long range asymmetric fiber interferometer based vibration sensors has been proposed. Firstly, a median filter is employed to remove noise and to acquire the endpoint of the vibration signal. Thus a much higher endpoint detection precision can be obtained. Secondly, the time-frequency features are acquired through the MODWT, which can further improve the time-frequency distribution resolution and processing efficiency. Thirdly, the time delay between features is obtained using a cross-correlation algorithm. Finally, the vibration position information is demodulated based on the time delay value. The performance of the proposed wavelet-based algorithm was assessed and compared to previous studies in an asymmetric dual laser Mach–Zehnder interferometer based vibration sensing system. And the proposed scheme presented good results in both the positioning accuracy and the efficiency in field tests. Specifically, a detection of 98.2% of positioning errors are distributed within the range of ±20 m at a sensing length of 82 km and a mean processing time of 135 ms is also achieved. Therefore, the proposed scheme can expand the areas of application fields for long range vibration sensing.