Structural Similarity Index Method Research Articles

Tool wear monitoring based on scSE-ResNet-50-TSCNN model integrating machine vision and force signals

In the realm of mechanical machining, tool wear is an unavoidable phenomenon. Monitoring the condition of tool wear is crucial for enhancing machining quality and advancing automation in the manufacturing process. This paper investigates an innovative approach to tool wear monitoring that integrates machine vision with force signal analysis. It relies on a deep residual two-stream convolutional model optimized with the scSE (concurrent spatial and channel squeeze and excitation) attention mechanism (scSE-ResNet-50-TSCNN). The force signals are converted into the corresponding wavelet scale images following wavelet threshold denoising and continuous wavelet transform. Concurrently, the images undergo processing using contrast limited adaptive histogram equalization and the structural similarity index method, allowing for the selection of the most suitable image inputs. The processed data are subsequently input into the developed scSE-ResNet-50-TSCNN model for precise identification of the tool wear state. To validate the model, the paper employed X850 carbon fibre reinforced polymer and Ti–6Al–4V titanium alloy as laminated experimental materials, conducting a series of tool wear tests while collecting pertinent machining data. The experimental results underscore the model’s effectiveness, achieving an impressive recognition accuracy of 93.86%. When compared with alternative models, the proposed approach surpasses them in performance on the identical dataset, showcasing its efficient monitoring capabilities in contrast to single-stream networks or unoptimized networks. Consequently, it excels in monitoring tool wear status and promots crucial technical support for enhancing machining quality control and advancing the field of intelligent manufacturing.

Fast Numerical Reconstruction of Integral Imaging Based on a Determined Interval Mapping

In this paper, a fast numerical reconstruction of the integral imaging based on a determined interval mapping is proposed. To reduce the computation time, the proposed method employs the determined interval mapping instead of the use of magnification. In the numerical reconstruction procedure, the acquired elemental image array (EIA) from the 3D object is displayed. The flipped elemental image (EI)s are numerically formed by the virtual pinhole array. Then, the determined interval depending on the reconstruction plane is calculated and applied to each flipped EI. These flipped EIs are shifted to match the determined interval at the reconstruction plane and superimposed together. After this superimposed image is divided by the number of the superposition, the position error between the location of the shifted EI and the pixel position of the reconstruction plane is corrected by interpolation. As a result, the refocused image depending on the reconstruction plane can be reconstructed rapidly. From the experimental result, we confirmed that the proposed method largely decreased the computation time compared with the conventional method. In addition, we verified that the quality of the reconstruction by the proposed method is higher than the conventional method by the use of the structural similarity index method.

Optical Properties Analysis of Scattering Media Based on GI-OCT Imaging

An optical coherence tomography (OCT) system based on the ghost imaging (GI) technique is developed for correctly imaging in scattering media. Usually, the scattering in the media leads to a decrease in the signal-to-noise ratio of the reconstructed image. This problem can be solved by using ghost imaging-OCT(GI-OCT), but the number of patterns required for GI reconstruction depends on the concentration of the scattering media. Therefore, studying the relationship between the intensity distribution in reconstructed images and the optical properties of scattering media is essential. In this study, image reconstruction is carried out in scattering media with a concentration of 0.0% to 1.4%, diluted from processed milk. Using the structural similarity index method (SSIM) to analyze the reconstruction condition, it is found that the target image can be reconstructed correctly when the SSIM value is more than 0.7. By analyzing the intensity distribution of the reconstructed image, the results show that the extinction coefficient of the scattering media is negatively correlated with the contrast of the reconstructed image and positively correlated with the scattering intensity. Their correlation coefficients are −0.94 and 0.99, respectively.

Adjacent age classification algorithm of yellow-feathered chickens based on multi-scale feature fusion

The age of yellow-feathered chicken is important to distinguish the freshness of meat quality in the trade of yellow-feathered chicken. To investigate whether the Convolutional Neural Network (CNN) model can be applied to the instar classification of yellow-feathered chickens, a multi-scale feature fusion model called Chicken_Age_Network (CANet) was proposed. The model uses Inception to construct a feature extraction layer and extract the feature information of each chicken face image to improve the classification accuracy of the model. First, the self-developed yellow-feathered chicken facial image collection application was used to collect images, and the yellow-feathered chicken image database was constructed by using day-age classification. Second, the standard face image of yellow-feathered chicken was obtained by using Structural Similarity Index Method detection(SSIM), image segmentation, background removal, and normalization. The adjacent age classification needs to extract more features, and CANet’s feature extraction layer based on multi-scale feature fusion can extract features of different sizes. Another advantage of CANet is that the GMP (Global Max Pooling) replaces the final fully connected layer of the general CNN to reduce parameters and optimize the network model. Finally, chicken face images of adjacent days of age were tested and compared on VGG13, VGG19, DenseNet121, DenseNet161, SE-ResNet-20, MobileNet V1, ShuffleNet G2, ResNet50, ResNet34 and CSPDenseNet121. Test results show that CANet can quickly and accurately identify the age of yellow-feathered chickens. The classification accuracy on CANet model is 96.29 %, which is better than VGG13 (93.09 %), VGG19 (95.08 %), ResNet50 (85.53 %), ResNet34 (91.14 %), DenseNet121 (93.03 %), DenseNet161 (93.33 %), MobileNet V1(86.06 %), ShuffleNet G2(93.18 %), CSPDenseNet121(84.76 %) and SE-ResNet-20 (83.86 %). In order to verify the generalization of the model, experiments were carried out on the public data set CIFAR-10. Results show that CANet has good generalization and can be applied to other classification problems.

Histogram Shifting-Based Quick Response Steganography Method for Secure Communication

Steganography is a tool which allows the data for transmission by concealing secret information in a tremendously growing network. In this paper, a novel technique quick response method (QRM) is proposed for the purpose of encryption and decryption. Existing system uses side match vector quantization (SMVQ) technique which has some challenges such as security issues and performance issues. To handle the security and performance issues, the proposed system uses two methods, namely, quick response method and shifting method. In the proposed system, encoding part calculates the performance for capacity, PSNR (peak signal-to-noise ratio), MSE (mean square error), and SSIM (structural similarity index method), and the decoding part calculates the performance of MSE (mean square error) and PSNR (peak signal-to-noise ratio). The shifting method is used to increase the data hiding capacity. In this system, the encryption part embeds the secret image using steganography and the decryption part extracts the original image. By analyzing and comparing the proposed system with the existing system, it is proved that the system proposed was much better than the existing systems.

Artificial intelligence generated solar farside magnetogram using conditional generative adversarial network

A solar flare occurs due to a magnetic field reconnection above the active region. The active region magnetic complexity observed in the magnetogram could be used as proxies for solar flare forecasting. It is also known that solar flares that occur from emerging active regions located near the solar disk eastern limb can still have an impact on the Earth. Therefore, magnetic observation of active regions in the solar farside is important to forecast east limb flares occurrences. This study utilizes the conditional Generative Adversarial Network (cGAN) model to generate Artificial Intelligence (AI) solar farside magnetogram. Our model was trained using the solar frontside observations dataset from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) 304 Angstrom as input images and SDO/Helioseismic and Magnetic Imager (HMI) magnetogram as output images. Our model generates solar farside magnetogram using solar farside observation from Solar Terrestrial Relations Observatory (STEREO)/Extreme Ultraviolet Imager (EUVI) 304 Angstrom. We also conducted validation on the similarity of our AI-generated magnetogram with the magnetogram observation from SDO/HMI using the Structural Similarity Index (SSIM) method. SSIM obtained an average similarity value of 0.61±0.06 for training data and 0.47±0.02 for validation data which contain active regions producing flares.

Implementation of image fusion model using DCGAN

Remote Sensing Images (RSI) are captured by the satellites. The quality of the RSIs primarily depends on environmental conditions and image-capturing device capability. Rapid development in technology leads to the generation of High- Resolution (HR) images from satellites. However, these images are to be processed in a scientific way for the best results. A new Image Fusion (IF) technique with the help of wavelets, Deep Convolutional Generative Adversarial Networks (DCGAN), was designed to get super-resolution images for satellite images. Residual Convolution Neural Network (ResNet) increases the fused image accuracy by minimizing the vanishing gradient problem. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Method (SSIM), Feature Similarity Index Method (FSIM), and Universal Image Quality (UIQ) are taken as the metrics for comparing the results with other models. The experimental results are better than previous methods and minimize the spatial and spectral losses during the fusion.

Various Approaches to the Quantitative Evaluation of Biological and Medical Data Using Mathematical Models

Biomedical data (structured and unstructured) has grown dramatically in strength and volume over the last few years. Innovative, intelligent, and autonomous scientific approaches are needed to examine the large data sets that are gradually becoming widely available. In order to predict unique symmetric and asymmetric patterns, there is also an increasing demand for designing, analyzing, and understanding such complicated data sets. In this paper, we focused on a different way of processing biological and medical data. We provide an overview of known methods as well as a look at optimized mathematical approaches in the field of biological data analysis. We deal with the RGB threshold algorithm, new filtering based on the histogram and on the RGB model, the Image J program, and the structural similarity index method (SSIM) approaches. Finally, we compared the results with the open-source software. We can confirm that our own software based on new mathematical models is an extremely suitable tool for processing biological images and is important in research areas such as the detection of iron in biological samples. We study even symmetric and asymmetric properties of the iron existence as a design analysis of the biological real data. Unique approaches for clinical information gathering, organizing, analysis, information retrieval, and inventive implementation of contemporary computing approaches are all part of this research project, which has much potential in biomedical research. These cutting-edge multidisciplinary techniques will enable the detection and retrieval of important symmetric and asymmetric patterns, as well as the faster finding of pertinent data and the opening of novel learning pathways.

DATA OPTIMIZATION OF RAIN RADAR AND ITS COVERAGE EXPANSION USING RADAR NETWORK

This research developed network of two Santanu rain radars of the Center for Atmospheric Science and Technology LAPAN installed in Bandung (SANTANU 1) and Sumedang (SANTANU 2). The objective is to get larger coverage area which also comply data each other. The first step is data optimation to each radar detection results, particularly due to rain attenuation, and then validation with the transportable radar image. The shape analysis on a rain object detected in the slice of both radar images indicates 0.8672 of R 2 which show the width of both objectcs has a good similarity. Data optimation due to rain attenuation using attenuation correction for regional weather radar (KA) and path-integrated attenuation (PIA) indicate that the reflectivity improvement by KA is 10 times better than PIA. The object detected by Santanu rain radar is similar to the transportable radar for reflectivity larger than 30 dBZ. The next step is integration of both rain radar images to create mosaic image and then validation using the transportable radar data. The production of the mosaic image is tried out using three methods: image stitching, averaging, and Cressman scheme. The best result is presented by Cressman scheme which show more integrated slice area and maintain the features of the object. However, the processing time of this scheme is 17 minutes which is longer than image stitching method. The processing time depend on the number of grid data and the hardware memory. Validation on the mosaic result by using transportable radar data by implementing structural similarity index (SSIM) method yields 0,32 of average value. The improvement on SSIM value is obtained if speckle removal is applied by recording 59% of enhancement

An improved image quality algorithm for exemplar-based image inpainting

Image inpainting is a common technique for repairing image regions that are scratched or damaged. This process involves reconstructing damaged parts and filling-in regions in which data/colour information is missing. There are many potential applications for image inpainting, such as repairing old images, repairing scratched images, removing unwanted objects, and filling-in missing areas. This paper develops an exemplar-based algorithm, one of the most important and popular image inpainting techniques, to fill-in missing regions caused by removing unwanted objects, image compression, scratches, or image transformation via the Internet. The proposed algorithm includes two phases of searching to select the best-matching information. In the first phase, the searching mechanism uses the entire image to find and select the most similar patches using the Euclidean distance. The second phase measures the distance between the location of the selected patches and the location of the patch to be filled. The performance of the proposed approach is evaluated through comprehensive experiments on several well-known images used in this area of research. The experimental results demonstrate the superior performance of the proposed approach over some state-of-the-art approaches in terms of quality in terms of both objective (using the peak signal-to-noise ratio (PSNR) as well as the structural similarity index method (SSIM)) and subjective (i.e., visual) measures.

An Efficient Computational Model for Assessing the Stability Characteristics of Electro-active Natural Bio-resources

Background: The properties of the natural bio-sensors as the fuel after treatment, is beneficial and considered as the most environmental friendly alternative. The microbial fuel cell will help in the bio electricity generation. To use them first, it is important to know the stability and the characteristics of such organic compound. The research presents the computational methods of assessment of stability and characteristics analysis of organic herbs, Syzygium and Citrus. Objective: MFC has a very vast research area and many scientists are rigorously working on MFCs. Here, we have explained research work related to what we have presented in the paper. Methods: To compute the stability of these microbial fuel cells, we have used two different methods on each herb, Structural Similarity Index Method (SSIM) and Graph Comparison using their Coordinates (GCC). Results: This research work provides the results of convergence towards the stability of herbs. Further, this section also presents the performance characteristics of the software algorithms and their comparative results to verify the outcomes of the herb characteristics using both methods. Conclusion: The proposed work is efficient in finding stability of MFCs on the selected herbs. The approach should work fine on other herbs as well. Machine Learning could have been much useful for this purpose if the availability of the data would have been much high.

A Spatial Near-Field Clutter Reduction Filter Preserving Tissue Speckle in Echocardiography.

Near-field (NF) clutter in echocardiography is depicted as a diffuse haze hindering the visualization of the myocardium and the blood-pool, thereby degrading its diagnostic value. Several clutter filters have been developed, which are limited in patients with contraction motion and rhythm anomalies, and in 3-D ultrasound (US). This study introduces a new NF clutter reduction method, which preserves US speckles required for strain imaging. The filter developed detects the NF clutter region in the spatial frequency domain. The filter employs an oriented, multiscale approach, and assumes the NF clutter to be predominantly present in the highest and lowest bandpass images. These bandpass images were filtered, whilst sparing features in the myocardium and NF clutter-free regions. The performance of the filter was assessed in a volunteer study, in ten 3-D apical and parasternal view acquisitions, and in a retrospective clinical study composed of 20 cardiac patients with different indications for echocardiography. The filter reduced NF clutter in all data sets, whilst preserving all or most of the myocardium. Additionally, it demonstrated a consistent enhancement of image quality, with an increase in contrast of 4.3 dB on average, and generated a clearer myocardial boundary distinction. Furthermore, the speckles were preserved according to the quality index based on local variance, the structural similarity index method, and normalized cross correlation values, being 0.82, 0.92, and 0.95 on average, respectively. Global longitudinal strain measurements on NF clutter reduced images were improved or equivalent compared to the original acquisitions, with an average increase in strain signal-to-noise ratio of 34%.

Invertible secret sharing: Using meaningful shadows based on Sorted Indexed Code

The need for secret sharing using meaningful shadow images is an important method to protect the transmitted data that does not attract the attention of adversary, which has become obvious in the past few years. The recovery of the cover image without any distortion and security of the embedded secret string efficiently needs to be addressed.This paper proposes an invertible (k,n) secret sharing scheme using meaningful shadow image based on Sorted Indexed Code (SIC) with high embedding capacity. The SIC helps to achieve better randomness between shadow images in terms of NPCR, the closeness of each objective assessment (i.e., PSNR, SSIM, NPCR, and UACI) between the shadows. Hence, by utilizing the SIC and polynomial based threshold secret sharing method the proposed scheme achieves an adequate visual quality of shadow images, complete recovery of the cover image, and increases the embedding capacity. The experimental results show the visual quality of image shares for camouflage purpose of steganography. The visual qualities of shadow images for the proposed scheme are validated through Peak-Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Method (SSIM) analysis. Further, the security of the proposed scheme is analyzed using dissimilarity measures; Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI).

A Study on the Effect of Canny Edge Detection on Downscaled Images

Nowadays user devices such as phones, tablets etc. allows processing the images with help of high-end applications and softwares developed. Most of the times, the images are downscaled to make them compatible with these end devices. This leads to the loss of image quality. This loss of information on downscaling an image results in distortion of edges and while zoomed in results into a blurred image. As the edge detection is a basic step for many image processing applications such as object detection, object segmentation, object recognition, etc. It is necessary to know the impact of edge detection on downscaled image. In this paper, we are using Canny Edge detection method to detect the edges. The original images are downscaled using different interpolation methods. Canny Edge detection is applied on original images and downscaled images to compare the distortion in the edges. We used Structural Similarity Index Method for comparison. We are also comparing execution time taken by Canny Edge Detection on different interpolation methods to check for optimal interpolation method. We observed that the distortion in edges and time efficiency differ for different interpolation methods which are detailed below in the result section. As blurring is also a disadvantage of downscaling, we are applying Gaussian Blur on the images to compare the blurring due to Gaussian blur technique and blurring due to downscaling.

Optimum Design of a Seawater Intrusion Monitoring Scheme Based on the Image Quality Assessment Method

Seawater intrusion monitoring is quite different from the conventional monitoring of groundwater pollution. In this study, a new optimization method for the seawater intrusion monitoring scheme in the transitional zone was proposed. The objective of optimization was to maximize effective information monitored. The structural similarity index method (SSIM) of the image quality assessment was innovatively used to establish a mathematical expression for the effective monitored information, and an optimization model was constructed based on this. Taken the Longkou city of China as the study area, a numerical simulation model of variable density groundwater was constructed. The Monte Carlo method was used to consider the influence of the sensitivity parameters uncertainty on the monitoring scheme design. To avoid repeatedly calling of simulation models in the process of Monte Carlo experiments, a surrogate model was constructed by using the kernel extreme learning machine (KELM). Finally, the optimization model was solved by the genetic algorithm to obtain the optimal monitoring scheme. The results showed that the input-output relationship of the numerical simulation model for variable-density groundwater can be well approximated by the KELM surrogate model. The monitoring scheme optimized by the above method can well reflect the real state of seawater intrusion. This study expands the method on the scheme designs for seawater intrusion monitoring.

MRI denoising using BM3D equipped with noise invalidation denoising technique and VST for improved contrast

Denoising of medical scanned images such as X-ray, MRI etc. is important stage in the medical use. To remove the noise from “magnetic resonance images” (MRI) is the attention of researchers to generate the MR images with high “signal-to-noise ratio” as well as with high spatial resolution. In this denoising technique, block-matching and 3-dimensional filtering (BM3D) method is used to denoise the MR images. Main steps used in BM3D are grouping, 3-dimensional discrete wavelet transformation and wavelet shrinkage. In the proposed method, noise invalidation denoising technique (NIDe) is used rather than hard thresholding. NIDe gives the threshold value automatically based on the data and noise characteristics and threshold value changes according to the characteristics of data i.e. wavelet coefficient of image. Before denoising MR images, variance stabilization transform (VST) discard the noise variance dependency of the MRI intensities. Combining block-matching and 3-dimensional filtering technique and VST make able the use of the BM3D technique for Magnetic Resonance Image denoising. After BM3D i.e. final denoised MR image, “contrast limited adaptive histogram equalization” technique is applied to increase the contrast of MR images which are denoised. Performance metrics such as “Peak Signal to Noise ratio”, “Root Mean Square Error”, “Mutual Information”, “Edge Entropy” and “Structural Similarity Index Method” are found out for “T1 weighted”, “T2 weighted” and “PD weighted” magnetic resonance images.

Low dose cone-beam computed tomography reconstruction via hybrid prior contour based total variation regularization (hybrid-PCTV).

Previously, we developed a prior contour based total variation (PCTV) method to use edge information derived from prior images for edge enhancement in low-dose cone-beam computed tomography (CBCT) reconstruction. However, the accuracy of edge enhancement in PCTV is affected by the deformable registration errors and anatomical changes from prior to on-board images. In this study, we develop a hybrid-PCTV method to address this limitation to enhance the robustness and accuracy of the PCTV method. Planning-CT is used as prior images and deformably registered with on-board CBCT reconstructed by the edge preserving TV (EPTV) method. Edges derived from planning CT are deformed based on the registered deformation vector fields to generate on-board edges for edge enhancement in PCTV reconstruction. Reference CBCT is reconstructed from the simulated projections of the deformed planning-CT. Image similarity map is then calculated between reference and on-board CBCT using structural similarity index (SSIM) method to estimate local registration accuracy. The hybrid-PCTV method enhances the edge information based on a weighted edge map that combines edges from both PCTV and EPTV methods. Higher weighting is given to PCTV edges at regions with high registration accuracy and to EPTV edges at regions with low registration accuracy. The hybrid-PCTV method was evaluated using both digital extended-cardiac-torso (XCAT) phantom and lung patient data. In XCAT study, breathing amplitude change, tumor shrinkage and new tumor were simulated from CT to CBCT. In the patient study, both simulated and real projections of lung patients were used for reconstruction. Results were compared with both EPTV and PCTV methods. EPTV led to blurring bony structures due to missing edge information, and PCTV led to blurring tumor edges due to inaccurate edge information caused by errors in the deformable registration. In contrast, hybrid-PCTV enhanced edges of both bone and tumor. In XCAT study using 30 half-fan CBCT projections, compared with ground truth, relative errors (REs) were 1.3%, 1.1% and 0.9% and edge cross-correlation were 0.66, 0.68 and 0.71 for EPTV, PCTV and hybrid-PCTV, respectively. Moreover, in the lung patient data, hybrid-PCTV avoided the wrong edge enhancement in the PCTV method while maintaining enhancements of the correct edges. Hybrid-PCTV further improved the robustness and accuracy of PCTV by accounting for uncertainties in deformable registration and anatomical changes between prior and onboard images. The accurate edge enhancement in hybrid-PCTV will be valuable for target localization in radiation therapy.

Grey relational analysis based adaptive smoothing parameter for non-local means image denoising

In non-local means (NLM) algorithm used for suppression of noise in digital images, the choice of smoothing or decay parameter is a critical issue, which affects the performance of NLM algorithm by influencing the amount of smoothing. Generally, the smoothing parameter in NLM algorithm is kept fixed for all pixels in an image, which provides blurring effects near important image details such as edges, textures etc. in an image. This paper presents a grey relational analysis based adaptive non-local means (GRANLM) algorithm to select an adaptive smoothing parameter for each pixel. It considers the grade of relations between reference patch and adjacent patches in a defined region centred at pixel using grey relational analysis (GRA). Experimental results on various standard images for different noise levels show that the proposed algorithm outperforms the traditional NLM algorithm and other NLM variants in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), visual quality and method noise.

Design of image steganographic architecture using quantum-dot cellular automata for secure nanocommunication networks

The objective of this article is to introduce an architecture that performs image steganography. The architecture is built on quantum dot cellular automata (QCA) technology. The reason behind increasing use of QCA device is that it provides low device density with small amount of power dissipated. To encode message inside an image, Least Significant Bit (LSB) based encoder/decoder steganography circuit has been proposed. To design this encoder/decoder circuit, a new QCA XOR gate has been designed. The proposed QCA XOR gate has low device density and less cell count compare to other existing circuits. The proposed circuit also requires less amount of power. The Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index Method (SSIM) are used to measure the embedded information is in the acceptable range or not. The design accuracy is confirmed through simulation results and power analysis performed.

Single-Plane Dual-Modality Tomography for Multiphase Flow Imaging by Integrating Electrical Capacitance and Ultrasonic Sensors

This paper presents an experimental study of a single-plane dual-modality tomography (DMT) system integrating electrical capacitance tomography (ECT) and ultrasonic tomography (UT) for water–oil–gas three-phase flow imaging. The soft-field (ECT) and hard-field (UT) systems measure different physical parameters, making them both a fine combination in complementing each system’s limitations. The DMT system consists of 16 units of customized ECT sensors and 16 units of UT transducers, which are composited on a single-plane arrangement to perform concurrent measurement. A finite-element method is used to model and study its feasibility and performance before engaging with the actual hardware. The DMT system is carried out on a vertical test column and horizontal stratified flow to obtain sensor data from each modality by inspection of separate tomographic image using linear back-projection (LBP) image reconstruction algorithm and convolution back projection with Gaussian filtering (CBPF). A fuzzy logic pixel fusion method is used for image fusion process. To evaluate the reconstructed images, the mean structural similarity index (MSSIM) method and the mutual information method ( $M_{F})$ are employed on both the LBP and CBPF image results. The analysis and assessments proved that the CBPF method has successfully improvised the image results and thus has better MSSIM rating and $M_{F}$ over the LBP method with an enhancement up to 12.55% and 8.92%, respectively.