Dynamic weight learning for RGB image demosaicking with a Bayer color filter array
Abstract
Most snapshot color imaging devices adopt a single sensor with a Bayer color filter array (CFA), where only one RGB component is captured at each pixel, requiring demosaicking to recover full-color images. Existing methods range from fast interpolation to deep learning approaches with high reconstruction accuracy but often suffer from a trade-off between performance and computational complexity. To address this issue, we propose an efficient demosaicking method based on dynamic weight learning. The proposed network adaptively computes layer-wise feature weights without increasing model parameters and incorporates a mixed-attention block to jointly exploit global and local information, effectively reducing reconstruction artifacts. Extensive experiments demonstrate superior performance over that of existing methods.
Fields of Study & Subjects
Image and Signal Denoising Methods
Advanced Data Compression Techniques
Image Enhancement Techniques
Publication details
- Journal / source
- Applied Optics
- Publisher
- Optica Publishing Group
- Volume
- 65
- Issue
- 12
- Pages
- 3968–3968
- Publication date
- 30 March 2026
- Language
- EN