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Demosaicking Algorithm Using Deep Residual Convolutional Network(IJIMAI, 2026-06-01) Jin Wang; Siyou Guo; Qilei Li; David Camacho; Gwanggil JeonSingle-sensor imaging systems are widely deployed in portable devices including digital cameras, smartphones, and personal digital assistants (PDAs) for real-time image acquisition. While convolutional neural networks (CNNs) have demonstrated exceptional capabilities in various image processing tasks, their potential for demosaicking applications remains underexplored. This paper presents a demosaicking framework utilizing a Deep Residual Convolutional Neural Network (DRCNN) architecture. Firstly, we initialize the mosaicked images using conventional demosaicking algorithms and learn the DRCNN for three color channels. The proposed DRCNN architecture innovatively integrates three core components: Binary Convolution Units (BCUs) for computational efficiency, Efficient Layer Aggregation Networks (ELAN) for multi-scale feature fusion, and Dense Residual Blocks (DRBs) for enhanced gradient flow. Comprehensive evaluations demonstrate that the proposed algorithms outperform existing approaches in PSNR, computational complexity, and visual quality.- Acceso Abierto
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Editor's Note(IJIMAI, 2026-06-01) Paulo Alonso Gaona-GarcíaRecent advances in Artificial Intelligence (AI) applied to Human–Computer Interaction (HCI) have been characterized by the convergence of immersive technologies, semantic reasoning, and data-centric learning paradigms. In augmented reality (AR), AI enables real-time interaction by integrating computer vision and contextual understanding into dynamic environments, although it simultaneously introduces new security vulnerabilities that require robust AI-driven protection mechanisms. In parallel, AI-driven cybersecurity has evolved toward proactive and adaptive models that leverage machine learning for real-time threat detection, anomaly analysis, and predictive mitigation, significantly enhancing the resilience of digital systems. Ontologies further strengthen this landscape by providing structured, semantically rich representations of cybersecurity knowledge, facilitating interoperability, automated reasoning, and intelligent threat assessment. Additionally, advances in visual analytics and time series foundation models are enabling the processing of large-scale temporal and multimodal datasets, supporting more sophisticated data interpretation and decision-making processes in complex environments. Modern computer vision systems leverage deep learning architectures such as convolutional neural networks and vision transformers to extract high-level features, enabling accurate object recognition, pattern detection, and scene understanding. Advances in multimodal AI systems—capable of integrating text, images, audio, and video—have become essential for achieving more comprehensive and context-aware intelligence, especially in domains such as medical imaging and interactive systems. Based on these advances highlight, this regular issue presents the most notable research in this direction. - Acceso Abierto
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Global Shin Buddhism and Ritual Practice. Histories, Transformations and Localisations(Routledge, 2026) Matsunaga, Louella; Galván-Álvarez, Enrique; Dake, MitsuyaThis book explores the globalisation of Shin Buddhism (also known as Jōdo Shinshū – the name by which it is referred to in this book) through an examination of ritual practice and its transformations in four main areas: Japan, the United States and Hawai’i, South America, and Europe. Interrogating conservative and mono-ethnic images of Shin Buddhism, linked to the preservation of Japanese identity in diaspora communities, it offers a complex picture of this form of Buddhism, as multi-ethnic, multi-lingual, and ever-changing. Drawing on historical sources, as well as fieldwork conducted in sites from three continents, the study shows the dynamic and transnational routes and local transformations of contemporary Shin Buddhism. The book further considers a range of ritual expressions of Shin Buddhism, including spatiality and architecture; music; embodiment and performance; and ritual adaptations to new virtual environments since the Covid-19 pandemic. All the authors are ordained Shin Buddhist priests, as well as academics, and bring a perspective which is informed both by academic research and by their first-hand experience of Shin Buddhism, including participation in ritual training. An analysis of Shin Buddhism as a global religion, this book will be of interest to students and scholars of religious studies, Anthropology, and the sociology of religion. It will also be of interest to researchers in Buddhist Studies and Asian Religions, in particular those interested in Buddhism as a world religion and Buddhist modernism.
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