Image demosaicing is an important step in the image processing pipeline for digital cameras. In data centric approaches, such as deep learning, the distribution of the dataset used for training can impose a bias on the networks' outcome. For example, in natural images most patches are smooth, and high-content patches are much rarer. This can lead to a bias in the performance of demosaicing algorithms. Most deep learning approaches address this challenge by utilizing specific losses or designing special network architectures. We propose a novel approach, SDAT, Sub-Dataset Alternation Training, that tackles the problem from a training protocol perspective. SDAT is comprised of two essential phases. In the initial phase, we employ a method to create sub-datasets from the entire dataset, each inducing a distinct bias. The subsequent phase involves an alternating training process, which uses the derived sub-datasets in addition to training also on the entire dataset. SDAT can be applied regardless of the chosen architecture as demonstrated by various experiments we conducted for the demosaicing task. The experiments are performed across a range of architecture sizes and types, namely CNNs and transformers. We show improved performance in all cases. We are also able to achieve state-of-the-art results on three highly popular image demosaicing benchmarks.

翻译：图像去马赛克是数字相机图像处理流程中的关键步骤。在以深度学习为代表的数据驱动方法中，训练数据集的分布特性会对网络输出结果产生偏差。例如，自然图像中多数图像块纹理平滑，而高纹理图像块较为稀少，这会导致去马赛克算法性能出现偏差。主流深度学习方法通常通过设计特定损失函数或特殊网络架构来应对该挑战。本文提出一种名为SDAT（子数据集交替训练）的新型方法，从训练协议角度解决该问题。SDAT包含两个核心阶段：初始阶段采用特定方法从完整数据集中构建多个子数据集，每个子数据集会引入不同的训练偏差；后续阶段执行交替训练流程，在利用子数据集训练的同时，也同步在完整数据集上进行训练。通过针对去马赛克任务的多项实验证明，SDAT可适用于任意网络架构。我们在不同规格与类型的网络架构（包括CNN和Transformer）上开展实验，结果显示所有场景下均获得性能提升。此外，该方法在三个主流图像去马赛克基准测试中均取得了当前最优结果。