Merging-ISP: Multi-Exposure High Dynamic Range Image Signal Processing

The image signal processing pipeline (ISP) is a core element of digital cameras to capture high-quality displayable images from raw data. In high dynamic range (HDR) imaging, ISPs include steps like demosaicing of raw color filter array (CFA) data at different exposure times, alignment of the exposures, conversion to HDR domain, and exposure merging into an HDR image. Traditionally, such pipelines are built by cascading algorithms addressing the individual subtasks. However, cascaded designs suffer from error propagations since simply combining multiple processing steps is not necessarily optimal for the entire imaging task. This paper proposes a multi-exposure high dynamic range image signal processing pipeline (Merging-ISP) to jointly solve all subtasks for HDR imaging. Our pipeline is modeled by a deep neural network architecture. As such, it is end-to-end trainable, circumvents the use of complex, hand-crafted algorithms in its core, and mitigates error propagation. Merging-ISP enables direct reconstructions of HDR images from multiple differently exposed raw CFA images captured from dynamic scenes. We compared Merging-ISP against different alternative cascaded pipelines. End-to-end learning leads to HDR reconstructions of high perceptual quality and quantitatively outperforms competing ISPs by more than 1 dB in terms of PSNR.