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Joint first and second order total variation decomposition for remote sensing images destriping Imaging Sci. J. (IF 0.871) Pub Date : 2024-02-22 Ayoub Boutemedjet, Sid Ahmed Hamadouche, Nabil Belghachem
Stripe noise remains a significant source of errors and image quality degradation in remote sensing systems. A prominent approach for tackling this problem is the first-order Total Variation (TV) r...
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A novel method for video enhancement under low light using BFR-SEQT technique Imaging Sci. J. (IF 0.871) Pub Date : 2024-02-13 J. Bright Jose, R. P. Anto Kumar
As typical frame rates allow limited exposure time, camera-captured videos under low-light conditions often suffer from poor contrast and noise. Existing models failed to consider dark and light ar...
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Face photo-line drawings synthesis based on local extraction preserving generative adversarial networks Imaging Sci. J. (IF 0.871) Pub Date : 2024-02-11 Yi Lihamu·Ya Ermaimaiti, Po Wang, Ying Tezhaer· Ai Shanjiang
Facial photo-to-sketch synthesis is crucial for entertainment and criminal investigations, yet challenges persist, including local detail blurring and identity feature loss. To mitigate these probl...
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Fractional Pelican African Vulture Optimization-based classification of breast cancer using mammogram images Imaging Sci. J. (IF 0.871) Pub Date : 2024-01-04 Rajesh Prasad, Jayashree Prasad, Nihar Ranjan, Amol Dhumane, Mubin Tamboli
Owing to the tiny abnormal developments in breast masses, examination and detection of breast cancer utilizing high-resolution images havebecome a complex process. Thus, an innovative framework for...
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Adaptive enhancement method of irregular low-pixel architectural design image based on lightness component Imaging Sci. J. (IF 0.871) Pub Date : 2023-12-15 Mei Qu
This study explores adaptive enhancement for irregular, low-pixel architectural design images, focusing on lightness components. Utilizing a median filter and wavelet threshold method removes image...
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Border Collie Shuffled Shepherd optimization-based image reconstruction using visual cryptography Imaging Sci. J. (IF 0.871) Pub Date : 2023-10-25 Sajitha A S, S. Sridevi Sathya Priya, Sanish V S
The visual secret sharing (VSS) technique is an encryption model to conceal secret messages into two or more meaningless images, called shares. Information loss is a huge drawback in existing syste...
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Greyscale correction algorithm of aerial filter array multispectral image Imaging Sci. J. (IF 0.871) Pub Date : 2023-10-18 Tong Shao Li, Wen Bang Sun, Xin Wei Bai, Di Wu, Zhen Hai Chen, Jia Yu Zhang
Filter array multispectral cameras are influenced by imaging mechanism and process characteristics, spliced images have edge interference fringes and greyscale differences. Aiming at the problems o...
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A low-light image enhancement method based on HSV space Imaging Sci. J. (IF 0.871) Pub Date : 2023-10-10 Libing Zhou, Xiaojing Chen, Baisong Ye, Xueli Jiang, Sheng Zou, Liang Ji, Zhengqian Yu, Jianjian Wei, Yexin Zhao, Tianyu Wang
To enhance the visual performance of low-illumination images, many low-illumination images are analyzed. Based on this, a low-light image enhancement method based on HSV space and semi-implicit ROF...
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Hybrid feature extraction and LLTSA-based dimension reduction for vein pattern recognition Imaging Sci. J. (IF 0.871) Pub Date : 2023-10-03 P. Gopinath, R. Shivakumar
In information and security, the personal identification of individuals becomes much more important. For improving security, several biometric recognition techniques are implemented. However, in fi...
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Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images Imaging Sci. J. (IF 0.871) Pub Date : 2023-10-05 Abirami S, Lanitha B
Brain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional...
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Wavelet energy-based adaptive retinex algorithm for low light mobile video enhancement Imaging Sci. J. (IF 0.871) Pub Date : 2023-09-24 G. R. Vishalakshi, A. Shobharani, M. C. Hanumantharaju
Our paper presents an adaptive multiscale retinex algorithm and a new wavelet energy metric to improve low-light video captured on mobile devices. Initially, we extract RGB frames from the video an...
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Optimization assisted autoregressive technique with deep convolution neural network-based entropy filter for image demosaicing Imaging Sci. J. (IF 0.871) Pub Date : 2023-09-20 C. Anitha Mary, A. Boyed Wesley
This paper presents an image demosaicing based on an optimization-driven deep learning model, namely the Autoregressive Water Wave Optimization algorithm (Autoregressive-WWO). The proposed method i...
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An implementation of intelligent YOLOv3-based anomaly detection model from crowded video scenarios with optimized ensemble pattern extraction Imaging Sci. J. (IF 0.871) Pub Date : 2023-09-13 Poorni Ramakrishnan, P. Madhavan
ABSTRACT The anomaly or abnormality detection in crowded scenes helps in identifying the violence and protecting the people from severe damage. Thus, there is a need to detect the anomalies with the classifier for learning information along with the usage of huge architectures. A new anomaly detection model is implemented in this model. The collected data is fed to optimal ensemble pattern extraction
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Multi-object 3D segmentation of brain structures using a geometric deformable model with a priori knowledge Imaging Sci. J. (IF 0.871) Pub Date : 2023-09-11 Mohamed Baghdadi, Nacéra Benamrane, Mounir Boukadoum, Lakhdar Sais
ABSTRACT Brain structure segmentation in 3D Magnetic Resonance Images is crucial for understanding neurodegenerative disorders. Manual segmentation is error-prone, necessitating robust automated techniques. In this paper, we introduce a novel and robust approach for the simultaneous segmentation of multiple brain structures in MRI images. Our method involves the concurrent evolution of 3D surfaces
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Matching evaluation based on image content discriminative features for different image types Imaging Sci. J. (IF 0.871) Pub Date : 2023-09-05 Eman S. Sabry, Salah Elagooz, Fathi E. Abd El-Samie, Nirmeen A. El-Bahnasawy, Ghada M. El-Banby, Rabie A. Ramadan
Search by image has taken the place of the prior wordy approach, considering spatial image comparison in the feature space. Several query image forms are available. Sketch-to-real image matching is...
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Hybrid deep learning technique for optimal segmentation and classification of multi-class skin cancer Imaging Sci. J. (IF 0.871) Pub Date : 2023-08-24 G. Subhashini, A. Chandrasekar
ABSTRACT This study introduces a novel deep learning-based approach for skin cancer diagnosis and treatment planning to overcome existing limitations. The proposed system employs a series of innovative algorithms, including IQQO for preprocessing, TSSO for cancer region isolation, and FA-MFC for data dimensionality reduction. The USSL-Net DCNN extracts hidden features, and the BGR-QNN enables multi-class
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3D reconstruction quality assessment using 2D reprojection with dynamic partitioning Imaging Sci. J. (IF 0.871) Pub Date : 2023-08-24 Camilo Chamorro-Rivera, Augusto Salazar-Jimenez
ABSTRACT In 3D reconstruction the stereo technique is one of the most used, generating point clouds of acceptable quality. One way to improve its quality is by fusing it with active systems such as lasers. For this fusion, a registration process can be used. It is important to evaluate the quality of the reconstruction in terms of spatial structure accuracy and visual appearance. A method of evaluating
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A novel framework of multimodal medical image fusion using adaptive NSST and optimized deep learning approach Imaging Sci. J. (IF 0.871) Pub Date : 2023-08-14 K. Vanitha, D. Satyanarayana, M. N. GiriPrasad
ABSTRACT Multimodal medical image fusion plays a pivotal role in the medical and imaging industry. Existing works of deep learning method suffers from blurred texture characteristics and computing efficiency. Thus, a novel deep learning model is proposed for multimodal medical image fusion. Initially, an Adaptive Non-Subsampled Shearlet Transform (ANSST) approach is developed for decomposing the images
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Tangent hybrid leader coronavirus herd optimization for the foreground and background image segmentation using multilevel thresholding Imaging Sci. J. (IF 0.871) Pub Date : 2023-08-09 R. Sowmiya, P. D. Sathya
ABSTRACT In this paper, multilevel thresholding with Tangent Hybrid Leader Coronavirus Herd Optimization (THLCHO) is introduced for the image segmentation of foreground and background, The input image is passed to the pre-processing that is done by the adaptive wiener filtering (AWF) technique and the Region of Interest (RoI) extraction. Multilevel thresholding approaches such as threshold and threshold
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Performance evaluation of linear and nonlinear filters for despeckling B mode foetal heart ultrasound images Imaging Sci. J. (IF 0.871) Pub Date : 2023-08-08 N. Sriraam, V. Punya Prabha, T. V. Sushma, S. Suresh
ABSTRACT Early detection of congenital heart disease (CHD), one of the most commonly occurring congenital defects, is important to reduce mortality rates. The major drawback of ultrasound imaging is the inherent speckle noise, making visual examination of anatomical structures a challenging task. This study discusses the effect of denoising using different linear and nonlinear filters on B mode foetal
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Gauss-Seidel based spatially varying optimal regularization improves reconstruction in diffuse optical tomography Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-31 Harish G. Siddalingaiah, Ravi Prasad K. Jagannath, Gurusiddappa R. Prashanth
ABSTRACT The inverse problem associated with Diffuse optical tomography image reconstruction is known to be highly nonlinear, under-determined, and ill-posed. The Levenberg-Marquardt technique is employed in solving it and is known to produce low-resolution reconstructed images. To stabilize the inversion of the large matrix, a heuristically chosen regularization parameter is used. A novel methodology
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Adaptive rule-based colour component weight assignment strategy for underwater video enhancement Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-27 Jitendra P. Sonawane, Mukesh D. Patil, Gajanan K. Birajdar
ABSTRACT Images and videos collected in an underwater environment often have low contrast, blur, and colour cast due to two significant sources of distortion; light scattering and absorption. In an underwater image/video, suspended particles attenuate red and blue components more than green channels. This article presents two adaptive weight allocation strategies based on rule assignment for red, green
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TW3-based ROI detection and classification using a chaotic ANN and DNN-EVGO architecture for an automated bone age assessment on hand X-ray images Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-18 Thangam Palaniswamy, Mahendiran Vellingiri, M. Ramkumar Raja
ABSTRACT Bone illnesses arise at a young age itself, so bone age assessment (BAA) is primarily utilized in paediatrics to identify their growth. Several BAA-related methods are employed to determine bone maturity; however, they do not give accuracy and the rate of error increases. To overcome this issue, in this manuscript, TW3-based Region of Interest (ROI) identification and classification with Chaotic
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A cooperative three-player game theory approach for designing an ideal video steganography framework Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-13 Suganthi Kumar, Rajkumar Soundrapandiyan
ABSTRACT This paper presents a cooperative game theory approach to improve the video steganography framework. Wherein, the video steganography framework comprises the following steps: (1) Cover video devising, (2) Secret image pre-processing, and (3) Embedding process. In the first step, the cover video is segmented using scene change detection method. Once the scenes are segmented the motion vectors
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A simple method for obtaining artificial 3D forms of 2D mammograms in diagnosis of breast cancer Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-13 Güliz Toz
ABSTRACT Breast cancer is one of the most common types of cancer among women worldwide and mammography is the primary method which plays a major role in early diagnosis of breast cancer. Mammograms can be obtained in two-dimensional (2D) or three-dimensional (3D) forms. 3D images contain more information than their 2D forms, however, they involve more computational and time complexities and are more
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An integrated region proposal and spatial information guided convolution network based object recognition for visually impaired persons’ indoor assistive navigation Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-05 Komal Mahadeo Masal, Shripad Bhatlawande, Sachin Dattatraya Shingade
ABSTRACT Multiple view object recognition is challenged by the impact of various view-angles on intra-class relationships. Visually impaired individuals can benefit from accurate navigation services with a navigation system that enables them to avoid obstacles to their destination. An indoor object detection framework called RSIGConv, based on an integrated Region proposal and Spatial Information Guided
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Protection of COVID-19 images using multiple elliptic curve cryptography Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-05 Diana Laishram, N. Tuturaja Singh, Khumanthem Manglem Singh
ABSTRACT COVID-19 pandemic has made the medical industry around the world under unprecedented and growing pressure. To preserve the integrity and protection of medical data for transmission and for secure diagnosis, this work suggests an encryption method for the protection of privacy of COVID-19 patient’s data by securing medical images based on elliptic curve cryptography. This is done through amalgamating
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Study and implementation of automated system for detection of PCOS from ultrasound scan images using artificial intelligence Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-03 M. Sumathi, P. Chitra, S. Sheela, C. Ishwarya
ABSTRACT Artificial Intelligence (AI), is a field of science and engineering that deals with intelligent behaviour which has the potential of improved access and the cost of healthcare applications.Polycystic Ovarian Syndrome (PCOS) is characterised by a protracted menstrual cycle and frequent excess androgen levels, which often affect many women of reproductive age. There are now no trustworthy objective
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A self-validation Noise2Noise training framework for image denoising Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-04 Asavaron Limsuebchuea, Rakkrit Duangsoithong, Jermphiphut Jaruenpunyasak
ABSTRACT Image denoising is a crucial algorithm in image processing that aims to enhance image quality. Deep learning-based image denoising methods can be categorized into supervised and unsupervised approaches. Supervised learning requires pairs of noisy and noise-free training data, which is impractical in real-world scenarios. Unsupervised learning uses pairs of noisy images for training, but it
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AlexNet-based deep convolutional neural network optimized with group teaching optimization algorithm (GTOA) for paediatric bone age assessment from hand X-ray images Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-03 E. P. Hemand, Mohandass G., Francis H. Shajin, D. Kirubakaran
ABSTRACT Bone age assessment is used to diagnose paediatric growth because some types of bone diseases occur in childhood. To overcome these issues, AlexNet-Based Deep Convolutional Neural Network Optimized with the Group Teaching Optimization Algorithm is proposed. First, input images are gathered via RSNA paediatric bone age dataset. These images are preprocessed using Wavelet Packet Transform Cochlear
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ADTBO: Aquila driving training-based optimization with deep learning for skin cancer detection Imaging Sci. J. (IF 0.871) Pub Date : 2023-07-03 Vadamodula Prasad, Emil Selvan G. S. R., Ramkumar M. P.
ABSTRACT Generally, melanoma skin disease is also a type of cancer, which is complex to determine. In case, various skin cancer diseases are identified at an early stage, then the death rate is to be reduced. Medical imaging technology acts as an important role in perceiving these types of skin lesions perfectly. This research introduced the automatic skin cancer detection technique, namely Aquila
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Region-based Convolutional Neural Network (R-CNN) architecture for auto-cropping of pancreatic computed tomography Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-30 Mamta Juneja, Gurunameh Singh, Chirag Chanana, Rishabh Verma, Niharika Thakur, Prashant Jindal
ABSTRACT Automatic pancreas detection and cropping with high precision from medical images is an important yet challenging problem for medical image analysis and Computer-Aided Diagnosis (CAD). Factors relating to the limited availability of image data and segmentation methodology hinder this task. High variability in the location of the pancreas,which occupies a very small area of the pancreatic Computed
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An automated hybrid attention based deep convolutional capsule with weighted autoencoder approach for skin cancer classification Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-28 R.P. Desale, P.S. Patil
ABSTRACT Skin cancer is a serious cancer caused by the uncontrollable growth of damaged DNA that leads to death. It is essential to identify the disease at the initial stage and eliminate it from spreading. Hence, this research introduces an automated hybrid deep learning (DL) technique for improving the accuracy of cancer diagnostic systems. In the pre-processing, histogram stretching, colour constancy
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An intelligent flower classification framework: optimal hybrid flower pattern extractor with adaptive dynamic ensemble transfer learning-based convolutional neural network Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-27 Suresh Anand. M, Korla Swaroopa, Manoj Nainwal, Therasa M
The rapid development in computer technology plays an essential role in the research works for performing fast and accurate identification of flower species through the processing of flower images ...
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Design of inception ResNet V2 for detecting malarial infection using the cell image captured from microscopic slide Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-27 P. Mayil Vel Kumar, Anita Venaik, P. Shanmugaraja, P. John Augustine, M. Madiajagan
ABSTRACT Over the past decades, malarial infection is considered a dreadful disease which ruins the lives of millions of people all over the globe. Several research works were developed based on machine learning algorithms to categorize the malarial infected person. However effective prediction with precise results is not attained in conventional approaches. For accurate prediction of malarial transmission
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An efficient glioma classification and grade detection using hybrid convolutional neural network-based SVM model Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-26 S. Shargunam, G. Rajakumar
Glioma develops in the brain and spinal cord. Oncologists frequently use ‘low-grade’ and ‘high-grade’ to describe how quickly malignant gliomas spread. Low-grade gliomas grow slowly, but still, the...
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Empirical mode decomposition and local binary pattern based feature extraction for face recognition Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-24 V. Betcy Thanga Shoba, I. Shatheesh Sam
ABSTRACT Facial recognition is a challenging pattern recognition problem in computer vision. This paper proposes a face recognition system that uses Empirical Mode Decomposition (EMD) and Local Binary Pattern (LBP) based feature extraction for a robust face recognition system. This scheme initially decomposes the image into 2N number of IMF (Intrinsic Mode Function) images, where N numbers of IMF images
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A time efficient offline handwritten character recognition using convolutional extreme learning machine Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-24 Raghunath Dey, Jayashree Piri, Dayal Kumar Behera, Asif Uddin Khan
ABSTRACT The Extreme Learning Machine (ELM) has sparked a lot of attention since it can learn fast and be applied to various problems. In this study, a convolutional layer-based extreme learning machine (CELM) architecture has been designed and implemented to recognize handwritten characters and reduce execution time. Furthermore, to validate the robustness of the approach, the characters are chosen
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Deep multi-scale three-dimensional convolutional neural network optimized with manta ray foraging optimization algorithm for classification of lung cancer on CT images Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-23 Veena V. Nair, C. S. Vinitha, Francis H. Shajin
ABSTRACT Lung cancer starts in the lungs and spreads to other organs in the body. Premature identification can only help the doctor to make an exact diagnosis and it may save the life of patients. Numerous studies have been conducted in this area, but none of them attains the accuracy outcomes. To overcome this drawback, a deep multi-scale three dimensional convolutional neural network optimized with
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Photonics radar based LSS targets’ postures’ m-D and cadence frequency imaging using empirical wavelet transform technique Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-23 Nargis Akhter, A. Arockia Bazil Raj, K. Prabu
ABSTRACT Developing a photonics radar to detect the combined multiple simultaneous-behaviours/postures of low-slow-small (LSS) aerial targets and appropriately extracting their Doppler profile have become significant to enable air surveillance/security and guidance for unmanned aviation. A CW photonic radar of 5.3 GHz, and an empirical wavelet transform (EWT) based radar signal processing algorithm
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Hybrid classification framework for chronic kidney disease prediction model Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-22 Smitha Patil, Savita Choudhary
ABSTRACT ‘Chronic kidney disease (CKD) – or chronic renal failure (CRF) is a term that encompasses all degrees of decreased kidney function, from damaged–at risk through mild, moderate, and severe chronic kidney failure’. As a risky factor, the disease has steadily turned out to be a major cause of death and morbidity. Accordingly, ultrasound (US) is significant in enhancing the rates of early recognition
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Optimization enabled deep learning approach with probabilistic fusion for image inpainting Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-22 Kingsley S, Sethukarasi T
ABSTRACT Nowadays, various deep learning (DL) approaches have been devised for image inpainting, which provided a substantial improvement in image quality. However, these approaches have failed to reconstruct the accurate structure of the original image. Hence, this research devised a novel and effective image inpainting approach, namely Autoregressive Flower Pollination Student Psychology Optimization
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Deep learning-based image watermarking technique with hybrid DWT-SVD Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-20 R. Radha Kumari, V. Vijaya Kumar, K. Rama Naidu
ABSTRACT In digital media copyright protection, image watermarking topologies afford a promising solution. But the robustness of watermarking methods should be considered. Therefore, a robust image watermarking technique has been proposed to show better robustness against rotation attacks and other issues in watermarking. In this work, the Deep Belief Network (DBN) is trained by the Bear Smell Search
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Multimodal medical image fusion using residual network 50 in non subsampled contourlet transform Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-20 K. Koteswara Rao, K. Veera Swamy
Medical image fusion technology and its collective diagnosis are becoming crucial day by day. This task confers the latest algorithm for image fusion of medical images to many diagnostic complicati...
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ALMEGA-VIR: face video retrieval system Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-19 T. Prathiba, R. Shantha Selva Kumari, M. Chengathir Selvi
ABSTRACT The limitations of Content Based Video Retrieval (CBVR), such as large pools of video data, selection of features, limited processing capacity, and content-related issues, can be overcome by Deep Belief Neural Networks (DBNs). The search engine does the processing, and the results are effectively returned to the users. The deep learning model also has some comparable challenges to be solved
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Dynamic method to optimize memory space requirement in real-time video surveillance using convolution neural network Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-08 Tamal Biswas, Diptendu Bhattacharya, Gouranga Mandal
ABSTRACT Real-time video surveillance is one of the most effective ways to observe crime, mischief, and violence. But, most of the recent surveillance system consumes huge memory space to store the video. This article proposed an advanced dynamic video surveillance strategy to utilize minimum memory space with the finest detection of suspicious moving objects. In the proposed system, a convolution
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Face mask detection in foggy weather from digital images using transfer learning Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-07 Isha Kansal, Vikas Khullar, Renu Popli, Jyoti Verma, Rajeev Kumar
ABSTRACT Community mask use is an efficacious non-pharmacologic way to minimize viral infection spread. It is a recommendation that individuals wear face masks as protective gear. Under ideal weather conditions, machine and artificial intelligence techniques can typically determine if a person is wearing a mask properly. Identification becomes more difficult under inclement weather such as fog, clouds
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Adam Bald Eagle optimization enabled transfer learning for underwater image fusion Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-07 Devika Sarath, Sucharitha M
ABSTRACT In this paper, a clear underwater image is attained by a fusion process using Transfer Learning (TL). Two images are selected from the underwater colour image dataset and those images are allowed to Discrete Wavelet Transform (DWT), Tetrolet transform and Saliency maps. Here, the outputs gained from images by the Tetrolet transform are fused and allowed for inverse Tetrolet transform. Moreover
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Graded fuzzy edge detection for imperceptibility optimization of image steganography Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-06 De Rosal Ignatius Moses Setiadi, Supriadi Rustad, Pulung Nurtantio Andono, Guruh Fajar Shidik
ABSTRACT The edge area of an image is more resilient to distortion caused by embedding in steganography, driving the advancement of edge detection-based methods. State-of-the-art techniques, including hybrid, dilation, and Fuzzy-based approaches, have been developed to enhance steganography performance. Typically, these methods categorize image pixels into two regions: edge and non-edge areas. This
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Texture-driven super-resolution of ultrasound images using optimized deep learning model Imaging Sci. J. (IF 0.871) Pub Date : 2023-06-06 M. Markco, S. Kannan
ABSTRACT While comparing to the additional medical imaging modalities, the low resolution (LR) with poor quality images are obtained because of natural intrinsic imaging characteristics. We proposed a novel deep learning- based super-resolution of ultrasound images that are texture-driven. In this study, Convolutional Neural Networks (CNNs) are used to speed up the process to increase the image quality
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Chronological-hybrid optimization enabled deep learning for boundary segmentation and osteoporosis classification using femur bone Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-26 Kiran Dhanaji Kale, Bharati Ainapure, Sowjanya Nagulapati, Lata Sankpal, Babasaheb Sambhajirao Satpute
This paper devises a technique for diagnosing and classifying osteoporosis using the femur bone’s X-ray images. The devised approach uses the following phases: image acquisition, pre-processing, se...
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CAHO-DNFN: ME-Net-based segmentation and optimized deep neuro fuzzy network for brain tumour classification with MRI Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-20 G. Neelima, Aravapalli Rama Satish, Balajee Maram, Dhanunjaya Rao Chigurukota
ABSTRACT A brain tumour is a deadly syndrome caused due to abnormal and uncontrolled expansion of extra cells that creates several tissues in the brain to affect the nervous system. It rapidly increases the growth of tumour cells and affects the brain by damaging or squeezing healthy tissues. Automatic brain tumour classification was done by conditional aquila horse herd optimization driven deep neuro
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A review of advances in image inpainting research Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-20 Hong-an Li, Liuqing Hu, Jun Liu, Jing Zhang, Tian Ma
ABSTRACT The aim of image inpainting is to fill in damaged areas according to certain rules based on information about the adjacent positions of missing areas and the overall structure of the image, a technique that plays a key role in various tasks in computer vision. With the rapid development of deep learning, researchers have combined it with image inpainting and achieved excellent performance
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EWPCO-enabled Shepard convolutional neural network for classification of brain tumour using MRI image Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-17 K. Mohana Sundaram, R. Sasikumar
ABSTRACT Numerous imaging techniques, like X-rays, Computerized Tomography (CT) scans, and ultrasound are utilized to predict brain tumours, but these imaging techniques experience difficulties in generating accurate results. To overcome such limitations, an effectual approach for the classification of brain cancer utilizing the proposed Exponentially Weighted Pelican Chimp Optimization-based Shepard
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Video compression using improved diamond search hybrid teaching and learning-based optimization model Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-16 B. Veerasamy, B. Bharathi, A. Ahilan
ABSTRACT Video compression is necessary to recreate a video without sacrificing quality. Nowadays, researchers are focusing on global optimization approaches to determine the optical flow of the neighboring pixels in video processing. In this work, a novel improved diamond search-hybrid teaching-learning based optimization (IDS-HTLBO) methodology has been proposed to compress the videos and increase
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K-Net-Deep joint segmentation with Taylor driving training optimization based deep learning for brain tumor classification using MRI Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-16 Vadamodula Prasad, Vairamuthu S, Selva Rani B
ABSTRACT Globally, a huge number of people succumb to brain tumour, which is considered to be one of the lethal types of tumours. In this research, an effective brain tumour segmentation and classification approach is implemented using Deep Learning (DL) based on Magnetic Resonance Imaging (MRI). Here, the segmentation of the tumour region from the brain image using the proposed hybrid K-Net-Deep joint
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An advanced fuzzy C-Means algorithm for the tissue segmentation from brain magnetic resonance images in the presence of noise and intensity inhomogeneity Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-16 Sandhya Gudise, K. Giri Babu, T. Satya Savithri
ABSTRACT Segmentation of brain Magnetic Resonance Images (MRIs) into various brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) is very important to detect and diagnose different brain-related disorders at the primitive level. Accurate segmentation of brain MRIs is very difficult because of the intricate anatomical structure of the tissues, the existence of Intensity
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Skull stripping on multimodal brain MRI scans using thresholding and morphology Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-15 Sajid Y. Bhat, Afnan Naqshbandi, Muhammad Abulaish
ABSTRACT This paper introduces a novel thresholding and morphology-based skull stripping method for different MRI modalities. The proposed method is designed in a way which is easy to use and generates satisfactory results with minimal parameter adjustments. The method is evaluated on three different benchmark datasets and compared with nine state-of-the-art skull stripping methods. The experimental
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Depthwise convolution based pyramid ResNet model for accurate detection of COVID-19 from chest X-Ray images Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-13 K. G. Satheesh Kumar, V. Arunachalam
ABSTRACT The global pandemic of coronavirus disease 2019 (COVID-19) causes severe respiratory problems in humans. The Chest X-ray (CXR) imaging technique majorly assists in detecting abnormalities in the chest and lung areas caused by COVID-19. Hence, developing an automatic system for CXR-based COVID-19 detection is vital for disease diagnosis. To accomplish this requirement, an enhanced Residual
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SHBO-based U-Net for image segmentation and FSHBO-enabled DBN for classification using hyperspectral image Imaging Sci. J. (IF 0.871) Pub Date : 2023-05-13 Tatireddy Subba Reddy, V. V. Krishna Reddy, R. Vijaya Kumar Reddy, Chandra Sekhar Kolli, V. Sitharamulu, Majjaru Chandrababu
ABSTRACT Hyper spectral imaging (HSI) is an advanced and fascinating remote sensing method in various domains. Every sample in HS remote sensing images possesses high-size features and has a massive amount of spatial and spectral data that enhances the complexity of feature selection and mining. Also, it improves the interpretational complications and thus surpasses the prediction accuracy of the system