Work

Efficient RGBW remosaicing using local interpolation and global refinement

Year: 2025

Type: article

Abstract: We propose an efficient RGBW remosaicing algorithm that converts RGBW images into Bayer images using learned kernel-based local interpolation and global residual learning. First, the proposed algorithm extracts local and global features from an input RGBW image. Then, we develop a learned kernel-based interpolation module to generate an intermediate Bayer image using the local features. Next, the proposed algorithm generates a residual image containing complementary information. Finally, we obtain the reconstructed Bayer image by refining the intermediate Bayer image with the residual image. Experimental results demonstrate that the proposed algorithm significantly outperforms state-of-the-art algorithms.

Source: ICT Express

DOI: 10.1016/j.icte.2025.09.010

Authors: Sanga Park, An Gia Vien, Chul Lee

Institutions: Dongguk University, Aalto University

Language: English

Topic: Advanced Image Processing Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,500

Funders: National Research Foundation of Korea, Ministry of Science and ICT, South Korea, Institute for Information and Communications Technology Promotion