"ENHANCING IMAGE SUPER-RESOLUTION USING DEEP LEARNING AND MATHEMATICAL OPTIMIZATION TECHNIQUES"

: Neurodegenerative diseases can make life difficult and lead to death in many cases. They also can be difficult, time-consuming, and costly to diagnose with enough accuracy/certainty. Artificial intelligence (AI) has shown promise in tackling some of the challenges present in medical imaging and is anticipated to become a crucial tool in health care applications in the near future. In particular, deep learning methods have displayed great performance in various subfields of image processing, including but not limited to image segmentation, image synthesis, and image reconstruction. In this paper, many state-of-the-art applications of deep learning models in image processing were reviewed.

Digital imaging is now ubiquitous, from the smart phone in your pocket to state-of-the-art medical imaging and satellite imagery. Raw imaging data can be manipulated using computer algorithms to perform a range of functions from reducing noise to complex image analysis and pattern and object recognition. The result is that digital image processing has applications in almost all areas of science and engineering. Designed for a one semester course, the aim of this book is to concentrate on the principles and techniques of image processing. This second edition includes important updates to the first edition, as well as two entirely new chapters, making the book ideal for advanced students in physics and engineering. Key features • A combination of theory, exercises and supportive software • Uses MATLAB to perform processing transformations – and accompanied by downloadable code • Programming exercises using MATLAB • New chapters on machine learning, error correcting codes and cryptography

In the domain of image processing, deep learning has assumed the position of the spearhead of innovation, creating revolutionary changes in numerous fields, such as image categorization, object recognition, and image generation. This white paper aims at developing a more detailed overview of the modern deep learning methods which are primarily designed for image processing. Analyzing the essential aspects of CNNs, GANs, and RNNs we wish to reveal innovative achievements in this area. In this white paper, it highlights a broad variety of domains, where these deep learning techniques have demonstrated remarkable progress. In the field of medical imaging, where CNNs help in the diagnosis and prognosis of the disease, to satellite imagery analysis, where GANs improve the generation of synthetic realistic data, and computer vision, where RNNs allow for better video analysis, deep learning is redefining the limits of image processing. Development should also be highlighted as an issue that the integration of deep learning into the image processing pipeline may bring along with it, including data accessibility, model interpretability, and computing resources. Accordingly, acknowledging such issues and staying updated with new trends, researchers and practitioners will be able to fully utilize deep learning and use it to solve complex image processing problems and carry out innovation in many different spheres of life. Deep learning is here to stay at the cutting edge of image processing, transforming our abilities and perspectives in this ever - changing discipline.

The rapid development of medical practices and imaging technology tools creates substantial growth in the amount of medical image data each year in our present era. This research aims to develop a hybrid approach that integrates Machine Learning (ML) and Deep Learning (DL) techniques to enhance the accuracy and reliability of medical image classification for diagnostic purposes. Medical imaging data complexity and growing volume serve as the research motivation, which leads to an investigation of standalone ML or DL limitations and their combination into a single framework. The medical image processing starts with normalization, then noise reduction, and continues to grayscale conversion before performing histogram equalization. This research uses VGG16 and ResNet50 alongside MobileNet and InceptionV3 for feature extraction, then applies ten different ML algorithms, including SVM and MLP, and Ran-dom Forest, for classification. Five public medical image datasets from Kaggle are used: COVID-19 chest X-rays, melanoma skin lesions, pneumonia chest X-rays, acute stroke facial images, and various eye diseases. Hybrid models display superior performance compared to stand-alone ML or DL models based on accuracy, precision, recall, and F1-score evaluation measures. Multiple datasets demonstrate that the MobileNet+MLP combination de-livers the most accurate results, which demonstrates its reliable and efficient performance. The developed AI diagnostic tool presents a scalable system alongside accuracy and interpretability to enhance clinical decision outcomes.

Quantum image processing represents a pivotal field in quantum remote sensing[1-3]. Dr. Siwen Bi and his research team pioneered the concept of quantum image data processing, having dedicated nearly 15 years to this technological advancement. This study focuses on developing core technologies for quantum-enhanced image processors, with particular emphasis on optimizing quantum enhancement algorithms, refining image processing methodologies, and implementing hardware solutions. The research aims to enhance both efficiency and quality in remote sensing and aerospace imaging applications[4-5], while providing efficient and precise image enhancement solutions for medical imaging, industrial inspection, remote sensing processing, and surveying fields. This study integrates quantum computing with traditional image processing technologies to develop quantum image enhancement algorithms, which have been applied to diverse imaging data types including remote sensing images, aerospace imagery, and medical imaging[6-8]. Through systematic research and experiments[9-11], we analyze key technologies in image enhancement processes such as quantum states, quantum superposition, quantum parallel computing, deep learning optimization, and hardware architecture design. Simultaneously, performance evaluations of various algorithms and hardware configurations were conducted using a combination of simulations and experimental methods. The research demonstrates that quantum image enhancement algorithms exhibit significant advantages over traditional methods in terms of processing accuracy, speed, and efficiency. Notably, image quality shows marked improvement during complex image processing. Furthermore, hardware-optimized quantum image processors can effectively support real-time processing of large-scale imaging data. As a cutting-edge image processing method, quantum image enhancement technology has demonstrated remarkable potential in remote sensing, aerospace, and medical fields[12-15]. Quantum computing offers innovative approaches to image processing, where the integration of deep learning and quantum computing can significantly enhance both efficiency and quality. With continuous advancements in quantum imaging hardware, this technology is poised to provide more efficient and precise solutions for image processing applications across various industries.

The development and structuring of an augmented reality (AR) platform integrating image processing, deep learning, and spatial tracking techniques have yielded promising results. AR technology, which enhances real-world objects with computer-generated information, holds vast potential for education, entertainment, and industry. Image processing enables real-time object detection, recognition, and tracking within the user's environment, while deep learning models enhance pattern recognition accuracy. Spatial tracking techniques ensure seamless integration of virtual content with the physical world, providing immersive AR experiences. Ant colony optimization (ACO) algorithms further enhance the AR platform's functionality by optimizing object placement and interaction. ACO mimics ant behavior to find optimal paths through graphs, effectively optimizing spatial layout and user interaction patterns in dynamic environments. The integration of ACO into the AR platform facilitates efficient pathfinding for augmented objects and enhances the system's responsiveness to user inputs. The successful implementation of the AR platform underscores its potential to revolutionize various industries, including healthcare, education, and marketing. By leveraging AI-driven algorithms and optimization techniques, AR systems can deliver personalized and engaging experiences that blur the boundaries between the digital and physical worlds. These results pave the way for future advancements in AR technology, driving innovation and reshaping human-computer interaction paradigms.

Modern artificial intelligence (AI) solutions often face challenges due to the "black box" nature of deep learning (DL) models, which limits their transparency and trustworthiness in critical medical applications. In this study, we propose and evaluate a scalable approach based on a transition matrix to enhance the interpretability of DL models in medical signal and image processing by translating complex model decisions into user-friendly and justifiable features for healthcare professionals. The criteria for choosing interpretable features were clearly defined, incorporating clinical guidelines and expert rules to align model outputs with established medical standards. The proposed approach was tested on two medical datasets: electrocardiography (ECG) for arrhythmia detection and magnetic resonance imaging (MRI) for heart disease classification. The performance of the DL models was compared with expert annotations using Cohen's Kappa coefficient to assess agreement, achieving coefficients of 0.89 for the ECG dataset and 0.80 for the MRI dataset. These results demonstrate strong agreement, underscoring the reliability of the approach in providing accurate, understandable, and justifiable explanations of DL model decisions. The scalability of the approach suggests its potential applicability across various medical domains, enhancing the generalizability and utility of DL models in healthcare while addressing practical challenges and ethical considerations.

Deep learning techniques have rapidly become important as a preferred method for evaluating medical image segmentation. This survey analyses different contributions in the deep learning medical field, including the major common issues published in recent years, and also discusses the fundamentals of deep learning concepts applicable to medical image segmentation. The study of deep learning can be applied to image categorization, object recognition, segmentation, registration, and other tasks. First, the basic ideas of deep learning techniques, applications, and frameworks are introduced. Deep learning techniques that operate the ideal applications are briefly explained. This paper indicates that there is a previous experience with different techniques in the class of medical image segmentation. Deep learning has been designed to describe and respond to various challenges in the field of medical image analysis such as low accuracy of image classification, low segmentation resolution, and poor image enhancement. Aiming to solve these present issues and improve the evolution of medical image segmentation challenges, we provide suggestions for future research.

A comparative study of X-ray and CT images in COVID-19 detection using image processing and deep learning techniques

In the medical field, it has been necessary to provide resources to detect ear-ly-stage diseases, including breast cancer. Deep learning is immersed in all aspects of medical image analysis, catapulting it as a possible dominant autonomous technology. In this systematic review, a total of 250 results were located, of which 40 were selected, for which a quantitative methodology with a descriptive basis was chosen. The objective of this bibliometric review is to analyze models in image processing for the early detection of breast cancer using deep learning. As result, digital mammography is the most effective method for detecting abnormalities in images. The research concludes that the application of CNN (Convolutional Neural Networks) is the most preferred choice of experts for medical image analysis due to its powerful pattern recognition and feature classifier.

Applications of Noncausal Gauss–Markov Random Field Models in Image and Video Processing

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Over the past few years, the healthcare industry has seen a dramatic increase in the use of intelligent automation enabled by AI technology. These developments are made to better the standard of medical decision making and the standard of treatment given to patients. Fuzzy boundaries, shifting sizes, and aberrations like hair or ruler lines all provide difficulties for automatic detection of skin lesions in dermoscopic images, slowing down the otherwise efficient process of diagnosing skin cancer. However, these difficulties may be conquered by employing image processing software. To address these issues, the authors of this paper provide a novel IMLT-DL model for intelligent dermoscopic image processing. Multi-level thresholding and deep learning are brought together in this model. Top hat filtering and inpainting have been included into IMLT-DL for use in image processing. In addition, Mayfly Optimization has been used in tandem with multilayer Kapur's thresholding to identify specific trouble spots. For further investigation, it uses an Inception v3-based feature extractor, and for data classification, it makes use of gradient boosting trees (GBTs). On the ISIC dataset, this model was shown to outperform state-of-the-art alternatives by a margin of 0.992% over the duration of trial iterations. These advances are a major step forwards in the quest for faster and more accurate skin lesion detection.

Deep Learning (DL) revolutionized a wide variety of research fields and applications of Artificial Intelligence (AI) in the last years. Computer vision is among those fields that witnessed a stronger impact, especially in some tasks like image equalization, classification and segmentation, that have several applications in medical imaging. Such applications often focus on microscopy images, but they can be successfully extended to other fields, like the detection of diseases involving the ocular surface and the cornea.One of the reasons of the success of deep learning applied to image processing relies in the end‐to‐end data‐driven approach. This means that the whole image processing and classification/segmentation pipeline can be adapted to the data being processed. This is particularly powerful when dealing with medical images, whose processing is often based on the detection of low‐ and mid‐level patterns that might be difficult to define in a formal manner. This means that deep learning has the chance to select and consider some image elements that would be nearly impossible to define using standard computer vision techniques. However, this becomes possible if large amounts of data are available for training the network, which became reality in the last years thanks to the wide digitalization of medical systems, including ophthalmology equipment.Based on a substantial amount of literature that has rapidly grown in the last years, this talk will focus on the new perspectives of eye disease diagnosis that were opened by the advent of deep learning. At the same time, the limitations and risks that must be considered in the development of DL‐based medical systems, like overfitting and lack of generalization, will be highlighted and discussed. Keeping track of such limitations is crucial in order not to consider DL as a sort of magic box, but rather, as powerful image analysis and processing tool.The talk will also provide a qualitative and quantitative evaluation of the diagnosis systems currently available that exploit DL and AI systems. The current and future impact of automatic image processing systems dedicated to ophthalmology will also be discussed. When automatic medical image analysis will be widely available, the patterns of medical diagnosis will need to change, taking advantage of the possibility to perform larger screenings that will be automatically processed.

Forged currency, a persistent threat to the stability of financial systems globally, demands sophisticated detection mechanisms. Traditional methods, reliant on manual inspection and basic image processing techniques, struggle to keep pace with the evolving sophistication of counterfeiters. As counterfeiters employ advanced technologies, including high-resolution printers and sophisticated printing techniques, the need for automated and reliable detection methods becomes increasingly critical. In response to these challenges, the emergence of deep learning technology has reshaped the landscape of image classification. Deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable capabilities in learning complex patterns and features directly from raw data. This breakthrough has found applications across diverse domains, including facial recognition, object detection, and medical imaging. By leveraging deep learning methodologies, the aim is to empower machines to autonomously discern subtle features and distinguish between genuine and counterfeit currency notes with high accuracy. The proposed project seeks to harness the power of deep learning, specifically CNNs, to develop an advanced fake currency detection system. Central to the project is the creation of a comprehensive dataset comprising a diverse range of currency images, encompassing both genuine and counterfeit notes. These images undergo meticulous preprocessing steps to ensure optimal input quality for the deep learning models. Leveraging this dataset, the models are trained, validated, and fine-tuned to optimize performance. In this work, it represents a concerted effort to harness cutting-edge technology in the fight against Forged currency. By leveraging deep learning methodologies and state-of-the-art architectures, it aims to create a formidable defense mechanism that safeguards financial systems against the ever-evolving threat of counterfeiters.