Image processing systems that are designed to work in real environments are subject to factors such as haze, rain, noise in sensors, motion blur, lack of proper illumination, and compression. Unlike the controlled environment, the aforementioned factors require an approach that can withstand various levels of degradations and still produce reliable results. In this chapter, a comprehensive discussion of how image processing systems have evolved, starting from basic signal-processing techniques to current advanced AI-based ones like CNNs, vision transformers, GANs, Retinex Enhancement Models, and Diffusion models is presented. Applications of these methods in autonomous vehicles, medicine, remote sensing, industry, and security surveillance are considered. Key issues such as computational complexity, dataset size, domain adaptation, and adversarial attacks are explored.
Pradeep Yadav, Jyoti Kumari, Sneha Arun Patil et al.· Advances in computational in...· 0 citations
This chapter examines three technology families that have drawn attention from supply chain researchers and practitioners: blockchain, augmented and virtual reality (AR/VR), and edge computing. The argument is not simply that each yields operational benefits, but that their significance lies in addressing complementary problems within a single domain. Blockchain addresses trust and information integrity in multi-party networks; AR/VR addresses human interface challenges in complex environments; edge computing provides distributed computation enabling real-time responsiveness at scale. Drawing on recent work, the chapter traces how these technologies support the industrial metaverse. Sustainability, resilience, human-centred design, and infrastructure scalability are key themes. It also examines challenges—interoperability, governance gaps, cost barriers, and security exposures—and offers a coherent view of their convergence and implications.
Ashish Gupta, Ergashev Nuriddin Gayratovich, Begimov Uktam et al.· Advances in computational in...· 0 citations