Demosaicing is crucial in digital imaging, reconstructing full-color images from partial red, green and blue sensor data with two-thirds of the pixel information missing. Although deep learning has improved performance, its large models and high computational demands are unsuitable for resource-limited edge devices. This work presents a green U-shaped image demosaicing (GUSID) method, a novel approach grounded in green learning (GL) principles. GUSID addresses the limitations of traditional methods by offering a lightweight, transparent and efficient solution. Unlike neural network-based methods, GUSID completely avoids deep learning. Instead, GUSID uses unsupervised representation learning for effective feature extraction and supervised feature learning to enhance computational efficiency and ensure high-quality performance. GUSID’s compact design minimizes computational overhead while maintaining competitive accuracy. Its support for parallelized training also enables fast execution, making it an ideal choice for real-time vision applications on resource-constrained devices.
Movahhedrad et al. (Tue,) studied this question.