In modern smartphone cameras, the Image Signal Processor (ISP) is the core
element that converts the RAW readings from the sensor into perceptually
pleasant RGB images for the end users. The ISP is typically proprietary and
handcrafted and consists of several blocks such as white balance, color
correction, and tone mapping. Deep learning-based ISPs aim to transform RAW
images into DSLR-like RGB images using deep neural networks. However, most
learned ISPs are trained using patches (small regions) due to computational
limitations. Such methods lack global context, which limits their efficacy on
full-resolution images and harms their ability to capture global properties
such as color constancy or illumination. First, we propose a novel module that
can be integrated into any neural ISP to capture the global context information
from the full RAW images. Second, we propose an efficient and simple neural ISP
that utilizes our proposed module. Our model achieves state-of-the-art results
on different benchmarks using diverse and real smartphone images.

现代智能手机相机中，图像信号处理器（ISP）是将传感器的原始读数转换为用户可感知的 RGB 图像的核心元素。我们提出了一个可以捕捉全局上下文信息的模块，并利用该模块构建了一个高效且简单的神经 ISP，实现了对不同基准测试集的全分辨率真实智能手机图像的最新成果。