Pixel-bin image sensors are becoming the default choice for smartphone cameras due to their resolution vs light-gathering trade-off. However, their larger inter-color separation compared to the Bayer color filter array (CFA) makes them challenging to demosaic. Furthermore, existing deep learning-based demosaicing methods are CFA-specific, requiring multiple individual models that take up precious onboard resources and demand larger development and maintenance efforts. In this work, we propose a modular unified architecture for demosaicing various pixel-bin sensors that provides higher image quality while being extensible and lightweight. Additionally, to enable plug-and-play operation, we introduce a learning-free CFA-identification module to detect the CFA type of raw data accurately.

翻译：像素分档图像传感器因其在分辨率与光收集能力间的权衡，正成为智能手机摄像头的默认选择。然而，与拜耳滤色器阵列（CFA）相比，其更大的色彩间隔使得去马赛克处理更具挑战性。此外，现有的基于深度学习的去马赛克方法具有CFA特异性，需要多个独立模型，这些模型占用宝贵的片上资源，并需要更大的开发和维护投入。本工作中，我们提出了一种模块化的统一架构，用于对多种像素分档传感器进行去马赛克处理，该架构在保持可扩展性和轻量化的同时提供更高的图像质量。此外，为实现即插即用操作，我们引入了一种无需学习的CFA识别模块，以准确检测原始数据的CFA类型。