论文标题
通过反向样式传输来逆转图像信号处理器
Reversing Image Signal Processors by Reverse Style Transferring
论文作者
论文摘要
原始图像数据集比低级视觉中的不良反问题的标准RGB图像数据集更合适，但在文献中并不常见。还有一些研究将重点放在将SRGB图像映射到原始格式上。从SRGB到RAW格式的映射可能是反向样式转移的相关域，因为该任务是一个不适的反向问题。在这项研究中，我们寻求答案：ISP操作是否可以建模为端到端学习管道中的样式因素？为了调查这个想法，我们提出了一种新型的架构，即RST-ISP-NET，以学习借助自适应特征归一化来逆转ISP操作。我们将此问题提出为反向样式转移，并且主要遵循先前工作中使用的实践。我们已经参与了AIM与我们建议的体系结构相反的ISP挑战。结果表明，将破坏性或修改因素建模为样式仍然有效的想法，但是需要进一步的改进才能在这种挑战中具有竞争力。
RAW image datasets are more suitable than the standard RGB image datasets for the ill-posed inverse problems in low-level vision, but not common in the literature. There are also a few studies to focus on mapping sRGB images to RAW format. Mapping from sRGB to RAW format could be a relevant domain for reverse style transferring since the task is an ill-posed reversing problem. In this study, we seek an answer to the question: Can the ISP operations be modeled as the style factor in an end-to-end learning pipeline? To investigate this idea, we propose a novel architecture, namely RST-ISP-Net, for learning to reverse the ISP operations with the help of adaptive feature normalization. We formulate this problem as a reverse style transferring and mostly follow the practice used in the prior work. We have participated in the AIM Reversed ISP challenge with our proposed architecture. Results indicate that the idea of modeling disruptive or modifying factors as style is still valid, but further improvements are required to be competitive in such a challenge.
