Demosaicking Algorithm Using Deep Residual Convolutional Network

Authors

DOI:

https://doi.org/10.9781/ijimai.2026.6568

Keywords:

Color Channel, Convolutional Neural Network, Demosaicking, Residual Learning
Supporting Agencies
This work was supported by Research Project Support Program for Excellence Institute (2022, ESL) in Incheon National University.

Abstract

Single-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.

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Author Biographies

Jin Wang, Incheon National University

Jin Wang received a BS in mathematics and applied mathematics from Zhejiang University in 2007, and an MS and PhD in electronic communications engineering from Hanyang University. She was a professor at Xidian University. She is a professor at Incheon National University.

Siyou Guo, Shandong University of Technology

Siyou Guo is pursuing an M.S. degree at the School of Electrical and Electronic Engineering, Shandong University of Technology, Zibo, China. His research interests include misinformation detection, deepfake detection, deep learning, and computer vision.

Qilei Li, Queen Mary University of London

Qilei Li received the Ph.D. in Computer Science from Queen Mary University of London. He previously earned an M.S. degree from Sichuan University in 2020. From June 2022 to April 2024, he worked as a machine learning scientist at Veritone Inc, where he focused on developing a scalable person search framework for retrieving individuals at different locations and times, as captured by various cameras. His current research interests lie in privacyaware machine learning, with a particular emphasis on learning domain-invariant knowledge representation from multimodal data captured in diverse environments. His research outcome has been recognized as ESI Highly Cited Paper (Top 1%). Additionally, he serves as an evaluator for the ELLIS PhD Program.

David Camacho, Universidad Politécnica de Madrid

David Camacho is full professor at Computer Systems Engineering Department of Universidad Politécnica de Madrid (UPM), and the head of the Applied Intelligence and Data Analysis research group (AIDA: https://aida.etsisi.uam.es) at UPM. He holds a Ph.D. in Computer Science from Universidad Carlos III de Madrid in 2001 with honors (best thesis award in Computer Science). He has published more than 300 journals, books, and conference papers. His research interests include Machine Learning (Clustering/Deep Learning), Computational Intelligence (Evolutionary Computation, Swarm Intelligence), Social Network Analysis, Fake News and Disinformation Analysis. He has participated/led more than 50 research projects (Spanish and European: H2020, DG Justice, ISFP, and Erasmus+), related to the design and application of artificial intelligence methods for data mining and optimization for problems emerging in industrial scenarios, aeronautics, aerospace engineering, cybercrime/cyber intelligence, social networks applications, or video games among others. He has served as Editor in Chief of Wiley’s Expert Systems since 2023 and sits on the Editorial Board of several journals, including Information Fusion, IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), Human-centric Computing and Information Sciences (HCIS), and Cognitive Computation among others.

Gwanggil Jeon, Incheon National University

Gwanggil Jeon received the B.S., M.S., and Ph.D. (summa cum laude) degrees from the Department of Electronics and Computer Engineering, Hanyang University, Seoul, Korea, in 2003, 2005, and 2008, respectively. From 2009.09 to 2011.08, he was with the School of Information Technology and Engineering, University of Ottawa,

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2026-02-24
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How to Cite

Wang, J., Guo, S., Li, Q., Camacho, D., and Jeon, G. (2026). Demosaicking Algorithm Using Deep Residual Convolutional Network. International Journal of Interactive Multimedia and Artificial Intelligence, 1–10. https://doi.org/10.9781/ijimai.2026.6568

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Regular Articles

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