International Journal of Image, Graphics and Signal Processing最新文献

{"title":"Image Denoising based on Enhanced Wavelet Global Thresholding Using Intelligent Signal Processing Algorithm","authors":"Joseph Isabona, Agbotiname Lucky Imoize, Stephen Ojo","doi":"10.5815/ijigsp.2023.05.01","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.05.01","url":null,"abstract":"Denoising is a vital aspect of image preprocessing, often explored to eliminate noise in an image to restore its proper characteristic formation and clarity. Unfortunately, noise often degrades the quality of valuable images, making them meaningless for practical applications. Several methods have been deployed to address this problem, but the quality of the recovered images still requires enhancement for efficient applications in practice. In this paper, a wavelet-based universal thresholding technique that possesses the capacity to optimally denoise highly degraded noisy images with both uniform and non-uniform variations in illumination and contrast is proposed. The proposed method, herein referred to as the modified wavelet-based universal thresholding (MWUT), compared to three state-of-the-art denoising techniques, was employed to denoise five noisy images. In order to appraise the qualities of the images obtained, seven performance indicators comprising the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Structural Content (SC), Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Method (SSIM), Signal-to-Reconstruction-Error Ratio (SRER), Blind Spatial Quality Evaluator (NIQE), and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) were employed. The first five indicators – RMSE, MAE, SC, PSNR, SSIM, and SRER- are reference indicators, while the remaining two – NIQE and BRISQUE- are referenceless. For the superior performance of the proposed wavelet threshold algorithm, the SC, PSNR, SSIM, and SRER must be higher, while lower values of NIQE, BRISQUE, RMSE, and MAE are preferred. A higher and better value of PSNR, SSIM, and SRER in the final results shows the superior performance of our proposed MWUT denoising technique over the preliminaries. Lower NIQE, BRISQUE, RMSE, and MAE values also indicate higher and better image quality results using the proposed modified wavelet-based universal thresholding technique over the existing schemes. The modified wavelet-based universal thresholding technique would find practical applications in digital image processing and enhancement.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information Technology for Multiparametric Analysis of Laser Images of Biological Fluid Films in Biomedical Applications","authors":"Yuriy Ushenko, Ivan Gordey, Yuriy Tomka, Irina Soltys, Oksana Bakun, Zhengbing Hu","doi":"10.5815/ijigsp.2023.05.06","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.05.06","url":null,"abstract":"At the current moment, all developed polarization methods utilize \"single-point\" statistical analysis algorithms for laser fields. A relevant task is to generalize traditional techniques by incorporating new correlation-based \"two-point\" algorithms for the analysis of polarization images. Theoretical foundations of the mutual and autocorrelation processing of phase maps of polarization-structural images of samples of dehydrated serum films are given. The maps of a new polarization-correlation parameters, namely complex degree of coherence (CDC) and complex degree of mutual polarization (CDMP) of soft matter layer boundary field by the example of dehydrated serum film samples are investigated. Two groups of representative samples, uterine myoma patients (control group 1) and patients with external genital endometriosis (study group 2), were considered. We applied a complex algorithm of analytical data processing - statistical (1stand 4th central statistical moments), correlation (Gram-Charlie expansion coefficients of autocorrelation functions) and fractal (fractal dimensions) parameters of polarization-correlation parameters maps. Objective markers for diagnosing extragenital endometriosis were found.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Denoising Self-Distillation Masked Autoencoder for Self-Supervised Learning","authors":"Jiashu Xu, Sergii Stirenko","doi":"10.5815/ijigsp.2023.05.03","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.05.03","url":null,"abstract":"Self-supervised learning has emerged as an effective paradigm for learning universal feature representations from vast amounts of unlabeled data. It’s remarkable success in recent years has been demonstrated in both natural language processing and computer vision domains. Serving as a cornerstone of the development of large-scale models, self-supervised learning has propelled the advancement of machine intelligence to new heights. In this paper, we draw inspiration from Siamese Networks and Masked Autoencoders to propose a denoising self-distilling Masked Autoencoder model for Self-supervised learning. The model is composed of a Masked Autoencoder and a teacher network, which work together to restore input image blocks corrupted by random Gaussian noise. Our objective function incorporates both pixel-level loss and high-level feature loss, allowing the model to extract complex semantic features. We evaluated our proposed method on three benchmark datasets, namely Cifar-10, Cifar-100, and STL-10, and compared it with classical self-supervised learning techniques. The experimental results demonstrate that our pre-trained model achieves a slightly superior fine-tuning performance on the STL-10 dataset, surpassing MAE by 0.1%. Overall, our method yields comparable experimental results when compared to other masked image modeling methods. The rationale behind our designed architecture is validated through ablation experiments. Our proposed method can serve as a complementary technique within the existing series of self-supervised learning approaches for masked image modeling, with the potential to be applied to larger datasets.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Face Recognition Using Modified Histogram of Oriented Gradients and Convolutional Neural Networks","authors":"Raveendra K","doi":"10.5815/ijigsp.2023.05.05","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.05.05","url":null,"abstract":"We are aiming in this work to develop an improved face recognition system for person-dependent and person-independent variants. To extract relevant facial features, we are using the convolutional neural network. These features allow comparing faces of different subjects in an optimized manner. The system training module firstly recognizes different subjects of dataset, in another approach, the module processes a different set of new images. Use of CNN alone for face recognition has achieved promising recognition rate, however many other works have showed declined in recognition rate for many complex datasets. Further, use of CNN alone exhibits reduced recognition rate for large scale databases. To overcome the above problem, we are proposing a modified spatial texture pattern extraction technique namely modified Histogram oriented gradient (m-HOG) for extracting facial image features along three gradient directions along with CNN algorithm to classify the face image based on the features. In the preprocessing stage, the face region is captured by removing the background from the input face images and is resized to 100×100. The m-HOG features are retrieved using histogram channels evenly distributed between 0 and 180 degrees. The obtained features are resized as a matrix having dimension 66×198 and which are passed to the CNN to extract robust and discriminative features and are classified using softmax classification layer. The recognition rates obtained for L-Spacek, NIR, JAFFE and YALE database are 99.80%, 91.43%, 95.00% and 93.33% respectively and are found to be better when compared to the existing methods.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Statistical Approach for Investigation and Comparison of Fatigue and Drowsiness based on Complexity Parameters of EOGs","authors":"Ashis Kumar Das, Prashant Kumar, Santanu Metia","doi":"10.5815/ijigsp.2023.05.04","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.05.04","url":null,"abstract":"The primary factors contributing to road accidents are drowsiness and fatigue. Additionally, it diminishes productivity within work environments and elevates the likelihood of accidents. The analysis of bio-signals is crucial in the examination of various physical conditions and the physiological state of an individual. Various biological signals were utilized to identify the presence of fatigue and drowsiness that is associated with fatigue. Various physiological signals were employed to identify driver or operator fatigue and drowsiness. Out of all these non-invasive signals, electrooculogram (EOG) exhibits well-accepted outcomes for detecting drowsiness and fatigue. By employing an EOG-based study, the real-time monitoring of the muscle and mental fatigue of the human subject can be done when they are engaging in their everyday activities. The present studies sought to employ a statistical analysis of electrooculograms (EOGs) to ascertain the stress levels of participants and provide insight into their state of fatigue and drowsiness. Two different experimental studies were performed with 120 and 80 healthy male and female research scholars of National Institute of Technology Durgapur, India. EOGs were recorded by the Biopac MP 45 data acquisition system at two and three different sessions of a day with huge cognitive tasks in between. Several entropies are evaluated from the time domain and frequency domain. The others complexity parameters are also incorporated to enrich the results of the experimental processes. An inferential statistical analysis based on the parametric t-test and non-parametric Wilcoxon test for study-I was considered to compare the stress levels between morning and evening sessions. Similarly, in study-II, the parametric ANOVA test and non-parametric Friedman test were carried out to monitor stress level in three different sessions of a day. The Tukey-Kramer post-hoc test is also undertaken to compare the outcomes among three different sessions and find the statistical differences based on a 5% significance level. Most complexity parameters show excellent results and clear differences in fatigue states for both the experiments and these analyses indicates the presence of onset fatigue among the subjects under consideration.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135252760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Variable Synergic Squeeze Convoluted Equilibrium Network Enabled Crowd Management in IoT","authors":"Jyoti A. Kendule, Kailash J. Karande","doi":"10.5815/ijigsp.2023.04.06","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.04.06","url":null,"abstract":": In IoT, Crowd counting is a difficult task, because of any sudden incidents people unites in a particular place. To count them effectively a crowd counting mechanism is needed. The crowd counting is help for public security. Several methods are proposed for crowd counting, but the existing methods does not provide high accuracy and high error rate. To overcome these drawbacks a Variable Synergic Squeeze Convoluted Equilibrium Network Enabled Crowd Management in IoT (VS 2 CEN-CC-IOT) is proposed in this manuscript for crowd counting and crowd density detection. Initially, the images are taken from two datasets named ShanghaiTech and Venice dataset. Then the images are preprocessed using Gaussian filter based preprocessing. After preprocessing the discrete wavelet transform (DWT) is used for extracting the features. The extracted features are then given to Synergic Squeeze Convoluted Equilibrium Network (SSCEN) for detecting crowd count and crowd density. In this work, variable Equilibrium Optimization Algorithm (EOA) is applied to optimize the weight parameter of SSCEN. The simulation procedure is performed in PYTHON platform. The V 𝑆 2 CEN-CC-IOT attains 0.8%, 1.3%, 1.5% higher accuracy, 13%, 3.3%, 8.2% higher Precision, 12%, 10%, 17% higher specificity , 8.2%, 3.3%, 6.9% higher F1-score and 0.12%, 0.06%, 0.07% lower mean absolute error (MAE), 0.2%, 0.25%, 0.1% lower root mean square error than the existing optimization approaches such as Arithmetic Optimization Algorithm(ADA), Chaos Game Optimization(CGO) and Gradient Based Optimizer(GBO) respectively.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116638539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Experimental and Statistical Analysis to Assess impact of Regional Accent on Distress Non-linguistic Scream of Young Women","authors":"Disha Handa, R. Vig, Mukesh Kumar, Namarta Vij","doi":"10.5815/ijigsp.2023.04.03","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.04.03","url":null,"abstract":": Scream is recognized as constant and ear-splitting non-linguistic verbal communication that has no phonological structure. This research is based on the study to assess the effect of regional accent on distress screams of women of a very specific age group. The primary goal of this research is to identify the components of non-speech sound so that the region of origin of the speaker can be determined. Furthermore, this research can aid in the development of security techniques based on emotions to prevent and report criminal activities where victims used to yell for help. For the time being, we have limited the study to women because women are the primary victims of all types of criminal’s activities. The Non-Speech corpus has been used to explore different parameters of scream samples collected from three different regions by using high-reliability audio recordings. The detailed investigation is based on the vocal characteristics of female speakers. Further, the investigations have been verified with bi-variate, partial correlation and one-way ANOVA to find out the impact of region-based accent non-speech distress signal. Results from the correlation techniques indicate that out of four attributes only jitter varies with respect to the specific region. Whereas ANOVA depicts that there is no significant regional impact on distress non-speech signals.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115782720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mammogram Pre-processing Using filtering methods for Breast Cancer Diagnosis","authors":"Shah Hemali, Agrawal Smita, P. Oza, S. Tanwar, A. Alkhayyat","doi":"10.5815/ijigsp.2023.04.04","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.04.04","url":null,"abstract":": Cancer is the second most found disease, and Breast cancer is the most common in women. Breast cancer is curable and can reduce mortality, but it needs to be identified early and treated accordingly. Radiologists use different modalities for the identification of Breast cancer. The superiority of Mammograms over other modalities is like minor radiation exposure and can identify different types of cancers. Therefore, mammograms are the most frequently used imaging modality for Breast Cancer Diagnosis. However, noise can be added while capturing the image, affecting the accuracy and analysis of the result. Therefore, using different filtering techniques to pre-process mammograms can enhance images and improve outcomes. For the study, the MIAS dataset has been used. This paper gives a comparative study on filters for Denoising and enhancement of mammograms. The study focuses on filters like Box Filter, Averaging filter, Gaussian Filter, Identical Filter, Convolutional 2D Filter, Median Filter, and Bilateral Filter. Performance measures used to compare these filters are Mean Squared Error (MSE), Structural Similarity Index Measure (SSIM), and Peak Signal-to-noise Ratio (PSNR). All Performance measures are evaluated for all images of MIAS dataset and compared accordingly. Results show that Gaussian Filter, Median Filter, and Bilateral Filter give better results than other filters.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114556903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep-ShrimpNet fostered Lung Cancer Classification from CT Images","authors":"V. Deepa, Mohamed Fathimal","doi":"10.5815/ijigsp.2023.04.05","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.04.05","url":null,"abstract":": Lung cancer affects the majority of people, due to genetic changes in lung tissues. Several existing methods on lung cancer detection are utilized with machine learning, but it does not accurately classify the lung cancer and also it takes high computation time. To overwhelm these issues, Deep-ShrimpNet fostered Lung cancer classification from CT images (LCC-Deep-ShrimpNet) is proposed. Initially, the input lung CT images are taken from IQ-OTH/NCCD Lung Cancer Dataset. Then the input lung CT images are pre-processed using Kernel co-relation method. Then these pre-processed lung CT images are given to Bayesian fuzzy clustering for extracting lung nodule region. Then the extracted lung nodule region is given into Deep-ShrimpNet classifier for representing features and classifying the lung CT images as normal (Healthy), Benign, and Malignant. The proposed LCC-Deep-ShrimpNet method is activated in python. The performance of the proposed LCC-Deep-ShrimpNet method attains 26.26%, 16.9%, 12.67%, 21.52% and 24.05% high accuracy, 68.86%, 59.57%, 57%, 62.72% and 65.69% low error rate and 60.76%, 53.67%, 68.58%, 59% and 56.61% low computation time compared with the existing methods.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124921160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radio Spectrum Measurement Modeling and Prediction based on Adaptive Hybrid Model for Optimal Network Planning","authors":"Seyi E. Olukanni, J. Isabona, I. Odesanya","doi":"10.5815/ijigsp.2023.04.02","DOIUrl":"https://doi.org/10.5815/ijigsp.2023.04.02","url":null,"abstract":": Path loss model is fundamental to effective network planning. It provides adequate information on the extent of signal loss and help to improve the quality of service of cellular communication in an area. In this paper we used a hybrid wavelet and improved log-distance model for modeling and prediction of propagation path loss in an irregular terrain. The prediction accuracy of the proposed model was quantified using five statistical metrics. As seen presented in Table 2 and Table 3, the proposed model outperformed the standard log-distance model, the COST234 Hata and Okumura Hata models by an average of 20%.","PeriodicalId":378340,"journal":{"name":"International Journal of Image, Graphics and Signal Processing","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133106401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}