统一架构解决像素合并传感器的去马赛克难题，轻量可扩展。
An Extensible and Lightweight Unified Architecture for Demosaicing Pixel-bin Image Sensors
- 模块化统一架构适配多种像素合并传感器
- 图像质量优于传统方法，资源占用更低
- 无需训练即可识别彩色滤光阵列类型，适合手机端部署
像素合并图像传感器因其在分辨率与采光能力间的平衡，正成为智能手机相机的主流选择。然而，其相对于Bayer彩色滤光阵列（CFA）更大的色间间距，给去马赛克带来挑战。现有基于深度学习的去马赛克方法多针对特定CFA设计，需多个独立模型，占用大量机载资源，开发与维护成本高。本文提出一种模块化、统一的去马赛克架构，可适配多种像素合并传感器，在保持轻量化的同时提升图像质量。此外，为实现即插即用，引入无需训练的CFA识别模块，能准确检测原始数据的CFA类型。
原文摘要 · Abstract (English)
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.
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