Special Issue

Recent Trends in Computational Photography and Image Synthesis Techniques

The adoption of computational photography cameras has been boosted by the rising popularity of social media platforms and the growing need for high-quality photos. As a result of improvements in smartphone camera technology, many smartphones now have computational photography features, which is fueling the market's expansion. Artificial intelligence integration is another development in the computational photography camera market. More creative and insightful image capture is made possible by AI integration.

Modern computational photography trends have witnessed an explosion of sophisticated methods targeted at boosting image quality, optimizing user experiences, and opening up new creative avenues. Convolutional neural networks (CNNs) are used for tasks including picture enhancement and color correction, producing outputs that are remarkably realistic. Deep learning has become the dominant force in this regard. Advanced image fusion algorithms and multi-camera systems in smartphones enable features like optical zoom, depth detection, and improved low-light performance. Computational imaging for augmented reality (AR) and virtual reality (VR) applications is also receiving more attention, it uses methods like depth estimation and real-time rendering to produce immersive experiences.

With the advancement of HDR imaging methods, pictures and videos can now have a greater dynamic range. Innovation in object detection, scene understanding, and live editing capabilities is driven by developments in real-time image processing, light field photography, and semantic picture understanding. For image synthesis, generative models like GANs are employed, and dedicated night modes and low-light imaging strategies are developed to achieve high-quality images under difficult lighting circumstances. In computational photography, privacy-preserving image solutions tackle data security and privacy issues.

Transformer-based models such as Vision Transformer improve synthesis results by capturing global context and long-range interdependence. Innovative algorithmic techniques and interdisciplinary partnerships are anticipated to propel innovation in image synthesis research as it progresses. The goal of inverse graphics techniques is to use 2D images to represent the underlying 3D scene geometry and lighting. Metalearning techniques enable the production of varied and superior images in several domains for image synthesis. However, this technology highlights the transformative influence of deep learning and inventive algorithms, shaping the future of visual content creation and technological innovation.

Potential topics include but are not limited to the following

• Analysis of latest breakthroughs in deep learning methodologies for image synthesis and computational photography.
• GAN architectures for producing realistic images and enhancing computational photography problems.
• Techniques for synthesizing images across multiple modalities.
• Advancements in enabling real-time image synthesis and computational photography on mobile devices and embedded systems.
• Transformer models for image synthesis tasks, includes generating high-resolution images and text-to-image synthesis.
• challenges and considerations in development and deployment of image synthesis techniques.
• cross-pollination of techniques and methodologies between computational photography and image synthesis.
• Emerging challenges and opportunities in computational photography and image synthesis.
• Tools and interfaces to actively participate in the image synthesis process.
• Analyzing methods for enhancing the robustness of computational photography systems.

Guest Editors

Managing Guest Editor

Dr.Arnold Adimabua Ojugo

Workplace: Department of Computer Science, Federal University of Petroleum Resources Effurun, Delta State, Nigeria.

Email: ojugo.arnold@fupre.edu.ng, arnoldadimabua@gmail.com

Website: https://scholar.google.com/citations?user=aEDkRagAAAAJ&hl=en

Research Interests: Ubiquitous Learning, Data Structures and Algorithms, Ubiquitous Computing, Computational Learning Theory, Computer systems and computational processes, Hardware Security

Biography: Arnold Adimabua Ojugo received his BSc in 2000, MSc in 2005 and PhD in 2013 – all in Computer Science from The Imo State University Owerri, The Nnamdi Azikiwe University Awka, and The Ebonyi State University Abakiliki respectively. He is an Associate Professor currently at Department of Computer Science (Federal University of Petroleum Resources Effurun) in Delta State, Nigeria. His research interests are in: Intelligent Systems, Machine-Learning, Performance and Ubiquitous Computing, Data Security and Graph Theory. He is also an Editor with the Progress for Intelligent Computation and Application, SciencePG Journals, and others. He is also a member of: Nigerian Computer Society, Computer Professionals of Nigeria and International Association of Engineers (IAENG).

First Co-Guest Editor

Dr.De Rosal Ignatius Moses Setiadi

Workplace: Faculty of Computer Science, Dian Nuswantoro University, Semarang City, Indonesia

Email: moses@dsn.dinus.ac.id

Research Interests: Artificial Intelligence, Computer Vision Image Steganography, Image Watermarking, Data Hiding, Image Encryption, Image Processing

Website: https://scholar.google.co.id/citations?user=tFeuHLcAAAAJ&hl=en

Biography: De Rosal Ignatius Moses Setiadi (Member, IEEE) is currently a Lecturer and Researcher at the Faculty of Computer Science, Dian Nuswantoro University, Semarang, Indonesia. He has authored or co-authored more than 150 peer reviewed journal and conference papers indexed by Scopus. He serves as the Editor-in-Chief for the Journal of Computing Theories and Applications (ISSN: 3024-9104), as well as an academic editor for the Security and Communication Journal and the Journal of Computer Networks and Communications at Hindawi-Wiley. Additionally, he is a member of the editorial board for the TEM (Technology, Education, Management, Informatics) Journal. He is also a reviewer for more than 60 Scopus-indexed journals.

Second Co-Guest Editor

Dr.Ayei Egu Ibor

Workplace: Research Associate, The Alan Turing Institute, London, United Kingdom.

Email: aibor@turing.ac.uk

Website: https://scholar.google.com/citations?user=DURiK8oAAAAJ&hl=en

Research Interests: Cybersecurity, Cyber Resilience, Cyber Threat Intelligence, Deep Learning, Digital Forensics

Biography: Ayei Egu Ibor received his B.Sc. in Computer Science from the University of Calabar in 2007. In 2012, he proceeded to the United Kingdom for his postgraduate studies where he received an M.Sc. in Computer Security and Forensics from the University of Bedfordshire in 2013. His PhD research, at the prestigious University of Lagos, is in cyber security with emphasis on mitigating multistage cyber-attacks. He is a Member of the Computer Professionals Registration Council of Nigeria (MCPN), a Chartered Information Technology Professional (CITP), Member, Nigeria Computer Society (MNCS), Member, Cyber Security Experts Association of Nigeria (MCSEAN), Member, Information Technology Systems and Security Professionals of Nigeria (MITSSP), and a Student Member of the British Computer Society. He is a highly innovative scientist, a peer reviewer, and an author with a good number of publications on cyber security approaches, algorithms and models in the areas of Machine Learning, Systems Hardening, Blockchain Technology, Data Mining, Deep Learning, and Cryptography, in refereed journals.

Timeline

• Manuscript submissions due: 31 Dec. 2025
• First round of reviews completed: 31 Jan. 2026
• Revised manuscripts due: 31 Feb. 2026
• Second round of reviews completed: 31 Mar. 2026