Deep learning-based image signal processor (ISP) models for mobile cameras can generate high-quality images that rival those of professional DSLR cameras. However, their computational demands often make them unsuitable for mobile settings. Additionally, modern mobile cameras employ non-Bayer color filter arrays (CFA) such as Quad Bayer, Nona Bayer, and QxQ Bayer to enhance image quality, yet most existing deep learning-based ISP (or demosaicing) models focus primarily on standard Bayer CFAs. In this study, we present PyNET-QxQ, a lightweight demosaicing model specifically designed for QxQ Bayer CFA patterns, which is derived from the original PyNET. We also propose a knowledge distillation method called progressive distillation to train the reduced network more effectively. Consequently, PyNET-QxQ contains less than 2.5% of the parameters of the original PyNET while preserving its performance. Experiments using QxQ images captured by a proto type QxQ camera sensor show that PyNET-QxQ outperforms existing conventional algorithms in terms of texture and edge reconstruction, despite its significantly reduced parameter count.

翻译：基于深度学习的移动相机图像信号处理器（ISP）模型能够生成媲美专业单反相机的高质量图像。然而，其计算需求往往使其难以适用于移动端场景。此外，现代移动相机采用非拜耳色彩滤波阵列（CFA），如Quad Bayer、Nona Bayer和QxQ Bayer以提升图像质量，但现有大多数基于深度学习的ISP（或去马赛克）模型主要针对标准拜耳CFA。本研究提出PyNET-QxQ，一种专为QxQ Bayer CFA模式设计的轻量级去马赛克模型，该模型源自原始PyNET。我们还提出一种称为渐进式蒸馏的知识蒸馏方法，用于更有效地训练精简网络。最终，PyNET-QxQ的参数数量仅为原始PyNET的2.5%以下，同时保持其性能。使用原型QxQ相机传感器捕获的QxQ图像进行的实验表明，尽管参数数量显著减少，PyNET-QxQ在纹理和边缘重建方面仍优于现有传统算法。