Simple Image Signal Processing using Global Context Guidance
This addresses the challenge of improving image quality in smartphone cameras by enhancing neural ISPs, though it is incremental as it builds on existing deep learning-based ISP methods.
The paper tackles the problem of deep learning-based Image Signal Processors (ISPs) lacking global context due to patch-based training, which limits performance on full-resolution images. It proposes a novel module for capturing global context and an efficient neural ISP, achieving state-of-the-art results on benchmarks with real smartphone images.
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.