Simple Image Signal Processing using Global Context Guidance
Omar Elezabi Marcos V. Conde Radu Timofte
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.