JDD-LI: Light and interpretable joint denoising and demosaicing

Joint denoising and demosaicing (JDD) aims to reconstruct high-quality color images from noisy RAW measurements. Recent deep learning approaches often rely on monolithic architectures with high computational cost. We propose JDD-LI, a lightweight modular JDD pipeline designed to improve the accuracy–efficiency trade-off. The method decomposes the task into three stages aligned with a simplified image signal processing pipeline: RAW-domain denoising, pretrained demosaicing, and RGB-domain denoising. The model is trained using a two-stage strategy where the demosaicer is first optimized under noise-free conditions and then fixed while the denoising modules are trained end-to-end. This design stabilizes training and simplifies optimization by decoupling the demosaicing and denoising stages. Experiments on Synthetic RAW and Mosaicked RGB benchmarks show competitive reconstruction quality with reduced computational cost. The modular structure also supports process-level interpretability through physically meaningful intermediate representations and ablation analysis.