ICTACT Journal on Image and Video Processing https://i-scholar.in/index.php/IJIVP ICTACT Journal on Image and Video Processing (IJIVP) is a peer – reviewed International Journal published quarterly. IJIVP welcomes Scientists, Researchers, Academicians and Engineers to submit their original research papers which is neither published nor currently under review by other journals or conferences. Papers should emphasize original results relating to both theoretical and application issues of Image and Video Processing. Review articles, focusing on multi disciplinary views, are also welcome. ICT Academy of Tamil Nadu en-US ICTACT Journal on Image and Video Processing 0976-9099 Enhancing Image Super-Resolution With Deep Convolutional Neural Networks https://i-scholar.in/index.php/IJIVP/article/view/224224 In computer vision, image super-resolution plays a pivotal role in improving the visual quality of low-resolution images, thereby enhancing various applications such as medical imaging, surveillance, and digital entertainment. The problem at hand involves the inherent limitations of conventional methods in restoring high-frequency information lost during image downscaling. This research aims to bridge this gap by leveraging DCNNs, exploiting their ability to learn complex mappings between low and high-resolution image spaces. This study addresses the challenge of image super-resolution through the application of Deep Convolutional Neural Networks (DCNNs). The research involves the design and training of a novel DCNN architecture tailored specifically for image super-resolution. We employ a large dataset of low and high-resolution image pairs to facilitate supervised learning. The network is trained to intelligently infer high-frequency details from low-resolution inputs, enabling the generation of visually compelling super-resolved images. Results from extensive experiments showcase the superior performance of the proposed DCNN-based approach compared to traditional methods. Quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSI), demonstrate significant improvements in image quality. Additionally, qualitative assessments highlight the network’s ability to reconstruct fine details, edges, and textures, resulting in visually pleasing super-resolved images. Archana Tomar Harish Patidar 2023-11-01 2023-11-01 14 Real-Time Object Detection in Videos Using Deep Learning Models https://i-scholar.in/index.php/IJIVP/article/view/224223 Video object detection plays a pivotal role in various applications, from surveillance to autonomous vehicles. This research addresses the need for real-time object detection in videos using advanced deep learning models. The current landscape of object detection techniques often struggles to maintain efficiency in processing video streams, leading to delays and resource-intensive computations. This study aims to bridge this gap by proposing a novel methodology for real-time object detection in videos. With the surge in video data across domains, the demand for swift and accurate object detection in real-time has become imperative. Existing methods face challenges in balancing speed and precision, prompting the exploration of more robust solutions. This research endeavors to enhance the efficiency of video object detection, offering a timely and accurate approach to address contemporary demands. The primary challenge lies in achieving real-time object detection without compromising accuracy. Traditional methods often compromise speed for precision, leading to inadequate performance in dynamic video environments. This study seeks to overcome this dilemma by introducing a methodology that optimizes both speed and accuracy, catering to the real-time constraints of video processing. Despite the advancements in object detection, a notable research gap exists in the domain of real-time video object detection. Existing models exhibit limitations in adapting to the dynamic nature of video streams, necessitating the development of novel methodologies. This research aims to fill this void by proposing an innovative approach that addresses the specific challenges posed by real-time video data. The proposed methodology integrates state-of-the-art deep learning models, optimizing them for real-time video object detection. Leveraging advanced architectures and streamlining the inference process, the model aims to provide accurate detections at unparalleled speeds. Additionally, a novel data augmentation technique is introduced to enhance the model’s adaptability to dynamic video scenarios. Preliminary results demonstrate the effectiveness of the proposed methodology, showcasing a significant improvement in both real-time processing speed and object detection accuracy. The model exhibits promising performance across diverse video datasets, highlighting its potential to outperform existing methods in real-world applications. M. Monika Udutha Rajender A. Tamizhselvi Aniruddha S. Rumale 2023-11-01 2023-11-01 14 Generative Adversarial Networks for Image Synthesis and Style Transfer in Videosgenerative Adversarial Networks for Image Synthesis and Style Transfer in Videos https://i-scholar.in/index.php/IJIVP/article/view/224225 In computer vision and artistic expression, the synthesis of visually compelling images and the transfer of artistic styles onto videos have gained significant attention. This research addresses the challenges in achieving realistic image synthesis and style transfer in the dynamic context of videos. Existing methods often struggle to maintain temporal coherence and fail to capture intricate details, prompting the need for innovative approaches. The conventional methods for image synthesis and style transfer in videos encounter difficulties in preserving the natural flow of motion and consistency across frames. This research aims to bridge this gap by leveraging the power of Generative Adversarial Networks (GANs) to enhance the quality and temporal coherence of synthesized images in video sequences. While GANs have demonstrated success in image generation, their application to video synthesis and style transfer remains an underexplored domain. The research seeks to address this gap by proposing a novel methodology that optimizes GANs for video-challenges, aiming for realistic, high-quality, and temporally consistent results. Our approach involves the development of a specialized GAN architecture tailored for video synthesis, incorporating temporal-aware modules to ensure smooth transitions between frames. Additionally, a style transfer mechanism is integrated, enabling the transfer of artistic styles onto videos seamlessly. The model is trained on diverse datasets to enhance its generalization capabilities. Experimental results showcase the efficacy of the proposed methodology in generating lifelike images and seamlessly transferring styles across video frames. Comparative analyses demonstrate the superiority of our approach over existing methods, highlighting its ability to address the temporal challenges inherent in video synthesis and style transfer. K. Ramesh B. Muni Lavanya B. Rajesh Kumar Narayan Krishan Vyas Mohammed Saleh Al Ansari 2023-11-01 2023-11-01 14 Deep Learning-Based Image Dehazing and Visibility Enhancement for Improved Visual Perception https://i-scholar.in/index.php/IJIVP/article/view/224226 In recent years, image dehazing has gained significant attention in the field of computer vision and image processing due to its crucial role in enhancing visibility and improving visual perception. The presence of haze in images captured under adverse weather conditions or polluted environments poses a challenge to various computer vision applications, such as autonomous driving, surveillance, and satellite imagery. Traditional image dehazing methods often struggle to achieve optimal results, particularly in complex scenes with varying degrees of haze and intricate details. The need for a robust and efficient dehazing approach has become imperative for addressing real-world challenges in computer vision applications. Despite the advancements in traditional methods, a research gap exists in developing a comprehensive solution that can handle diverse atmospheric conditions and complex scenes effectively. The integration of deep learning techniques presents an opportunity to bridge this gap, leveraging the power of neural networks to learn and adapt to intricate patterns in hazy images. This research proposes a novel deep learning-based approach for image dehazing and visibility enhancement. A Convolutional Neural Network (CNN) architecture is designed to learn complex relationships between hazy and clear images, allowing the model to effectively remove haze and enhance visibility. The network is trained on a diverse dataset encompassing various atmospheric conditions and scene complexities to ensure generalization. Experimental results demonstrate the superior performance of the proposed deep learning approach compared to traditional methods. The model exhibits robustness in handling challenging scenarios, achieving significant improvements in image clarity, contrast, and overall visibility. The findings highlight the potential of deep learning in addressing the limitations of existing dehazing techniques. Vidyabharathi Dakshinamurthi G. P. Suja P. Murugan Sk. Riyaz Hussain 2023-11-01 2023-11-01 14 COV-CT-NET - A Deep Learning Model for COVID-19, Community-Acquired Pneumonia Detection Using CT Images https://i-scholar.in/index.php/IJIVP/article/view/224227 The world has witnessed the deadly impact of the Novel Corona Virus (COVID-19), claiming millions of lives since its outbreak in early December 2019. Early virus detection plays a crucial role in controlling this highly contagious disease. Though Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the current standard for confirmation of COVID-19, it is time-consuming. Computed Tomography (CT) imaging of the lungs can preferably be used for fast diagnosis of the disease as it is more sensitive and can detect complications. Due to the unavailability of adequate expertise, a deep learning-based model on CT images is a potential solution for fast detecting SARS Cov2 virus. In this study, we developed a simple but robust Convolution Neural Network model with multiclass detection ability between normal lungs, COVID-19 infected lungs and any other Community-Acquired Pneumonia (CAP) infection using Chest CT images. It is tested on a publicly available dataset, COVID-CT-MD and it achieved slice level accuracy of 99% on test dataset. We also attempted slice-level prediction of the unlabelled slices available in the dataset of COVID-19 and CAP cases. Abul Hasnat Mangalmay Das Santanu Halder Debotosh Bhattacharjee 2023-11-01 2023-11-01 14 AI-Based Video Summarization for Efficient Content Retrieval https://i-scholar.in/index.php/IJIVP/article/view/224228 The explosive growth of video data poses a significant challenge in retrieving relevant content swiftly. Existing methods often fall short in providing concise yet informative summaries and efficient retrieval mechanisms. The primary issue lies in the overwhelming volume of video data, making it cumbersome for users to identify and access pertinent information efficiently. Traditional summarization techniques lack the sophistication to capture the nuances of video content, leading to a gap in effective content retrieval. Our approach involves training a Deep Belief Network (DBN) to autonomously generate concise yet comprehensive video summaries. Simultaneously, the Radial Basis Function (RBF) is employed to develop an efficient content retrieval system, leveraging the learned features from the video summarization process. The integration of these two methods promises a novel and effective solution to the challenges posed by the burgeoning volume of video content. Preliminary results demonstrate a significant improvement in the efficiency of content retrieval, with the integrated DBN and RBF approach outperforming traditional methods. The video summaries generated by the DBN exhibit enhanced informativeness, contributing to more accurate and rapid content retrieval. Kaavya Kanagaraj Shilpa Abhang Julakanti Sampath Kumar R. K. Gnanamurthy V. Balaji 2023-11-01 2023-11-01 14 Semantic Segmentation in Medical Image Analysis With Convolutional Neural Networks https://i-scholar.in/index.php/IJIVP/article/view/224229 Medical image analysis plays a pivotal role in modern healthcare, aiding clinicians in accurate diagnosis and treatment planning. However, the complexity and diversity of medical images pose significant challenges for traditional image processing methods. Existing methods often struggle to precisely delineate structures in medical images, leading to suboptimal diagnostic accuracy. The demand for automated and accurate segmentation tools in medical imaging has grown, highlighting the necessity for robust and efficient algorithms capable of handling diverse anatomical variations and pathologies. While CNNs have shown promise in image analysis, their application to medical images requires customization to accommodate unique challenges. The literature lacks comprehensive studies that bridge the gap between general-purpose CNNs and the specific demands of medical image segmentation, especially concerning the diverse and intricate structures present in medical imagery. This study addresses the need for advanced techniques by leveraging Convolutional Neural Networks (CNNs) for semantic segmentation in medical image analysis. Our approach involves the design and implementation of a specialized CNN architecture tailored to the nuances of medical image data. We employ state-of-the-art techniques for data preprocessing, model training, and validation. The model is trained on a diverse dataset encompassing various medical imaging modalities, ensuring its adaptability and generalizability. The proposed CNN-based semantic segmentation model demonstrates superior performance in accurately delineating anatomical structures compared to traditional methods. Evaluation metrics, including Dice coefficient and sensitivity, indicate the model efficacy in achieving precise segmentation. The results underscore the potential of CNNs in advancing medical image analysis for improved clinical outcomes. Shweta Nishit Jain Priya Pise Akhilesh Mishra 2023-11-01 2023-11-01 14 Machine Learning-Based Facial Recognition for Video Surveillance Systems https://i-scholar.in/index.php/IJIVP/article/view/224230 Video surveillance systems play a crucial role in ensuring public safety and security. However, the traditional methods of surveillance often fall short in effectively identifying individuals, particularly in crowded or dynamic environments. This research addresses the limitations of conventional video surveillance by proposing a machine learning-based facial recognition system. The increasing demand for robust security measures necessitates the development of advanced technologies in video surveillance. Facial recognition has emerged as a promising solution, but existing systems struggle with accuracy and efficiency. This research aims to bridge these gaps by leveraging machine learning techniques for facial recognition in video surveillance. Conventional video surveillance struggles with accurate and rapid identification of individuals, leading to potential security lapses. This research addresses the challenge of enhancing facial recognition accuracy in real-time video feeds, especially in scenarios with varying lighting conditions and occlusions. While facial recognition has gained traction, there is a significant research gap in the implementation of machine learning algorithms tailored for video surveillance. This study aims to fill this void by proposing a novel methodology that combines deep learning and computer vision techniques for robust facial recognition in dynamic environments. The proposed methodology involves training a deep neural network on a diverse dataset of facial images to enable the model to learn intricate facial features. Additionally, computer vision algorithms will be employed to handle challenges such as occlusions and varying lighting conditions. The model's performance will be evaluated using real-world video surveillance data. Preliminary results demonstrate a significant improvement in facial recognition accuracy compared to traditional methods. The machine learning-based system exhibits enhanced performance in challenging scenarios, showcasing its potential for practical implementation in video surveillance systems. Dileep Pulugu P. Anusha Ratan Rajan Srivastava R. Kalaivani Subharun Pal 2023-11-01 2023-11-01 14 Image Representation and Rendering From Low-Resolution Surveillance Videos Using Densenet https://i-scholar.in/index.php/IJIVP/article/view/224231 In surveillance, the need for enhanced image representation and rendering from low-resolution videos is paramount for effective analysis and decision-making. This research addresses the limitations of conventional methods in extracting meaningful information from low-resolution footage. The prevalent challenge lies in the compromised clarity and detail inherent in surveillance videos, hindering accurate identification and analysis of critical events. The ubiquity of surveillance cameras has led to an influx of low-resolution videos, limiting the efficacy of traditional image processing techniques. This research aims to bridge this gap by leveraging DenseNet, a densely connected convolutional neural network (CNN) known for its ability to capture intricate features. The DenseNet seeks to enhance the representation and subsequent rendering of images, transcending the constraints imposed by low resolutions. The network ability to capture intricate details will be harnessed to enhance image representation. Subsequent rendering techniques will be employed to reconstruct high-quality images for improved analysis. The results showcase promising advancements in image representation and rendering using DenseNet. The enhanced visual quality of surveillance images allows for more precise identification and analysis of events, demonstrating the potential impact of the proposed methodology on improving surveillance systems. D. C. Jullie Josephine B. Yuvaraj Sathesh Abraham Leo S. Thumilvannan 2023-11-01 2023-11-01 14 Image and Video Anomaly Detection Using AI Based Deepanomaly Detectors https://i-scholar.in/index.php/IJIVP/article/view/224232 In computer vision and anomaly detection, this research delves into the application of AI-based Deep Anomaly Detectors for the identification of anomalies in images and videos. The escalating growth of digital content necessitates robust and efficient methods for anomaly detection to ensure the integrity and security of visual data. As the volume of visual data continues to surge, conventional anomaly detection methods fall short in addressing the complexities inherent in images and videos. Traditional anomaly detection methods often struggle with the nuanced patterns and variations present in images and videos. The need for a more sophisticated and adaptive approach becomes imperative to identify anomalies accurately amidst the vast and diverse landscape of visual data. This study addresses this gap by leveraging the power of artificial intelligence, specifically Deep Anomaly Detectors, to enhance the accuracy and speed of anomaly detection in visual content. This research aims to bridge this gap by proposing a novel methodology that combines deep learning techniques with anomaly detection to achieve superior results in identifying anomalies in visual content. The proposed methodology involves the utilization of state-of-the-art deep learning architectures, training on a diverse dataset of images and videos to capture intricate patterns associated with anomalies. The model is then fine-tuned to enhance its sensitivity to deviations from normal visual patterns, ensuring a robust anomaly detection system. The results showcase a significant improvement in anomaly detection accuracy compared to traditional methods. The AI-based Deep Anomaly Detector exhibits a high level of sensitivity and specificity, effectively distinguishing anomalies in real-world scenarios, thus validating the efficacy of the proposed method. M. Elavarasi R. Pramodhini M. Deshmukh Deepak R. Mekala Chamandeep Kaur 2023-11-01 2023-11-01 14