  We present DeepISP, a full end-to-end deep neural model of the camera image
signal processing (ISP) pipeline. Our model learns a mapping from the raw
low-light mosaiced image to the final visually compelling image and encompasses
low-level tasks such as demosaicing and denoising as well as higher-level tasks
such as color correction and image adjustment. The training and evaluation of
the pipeline were performed on a dedicated dataset containing pairs of
low-light and well-lit images captured by a Samsung S7 smartphone camera in
both raw and processed JPEG formats. The proposed solution achieves
state-of-the-art performance in objective evaluation of PSNR on the subtask of
joint denoising and demosaicing. For the full end-to-end pipeline, it achieves
better visual quality compared to the manufacturer ISP, in both a subjective
human assessment and when rated by a deep model trained for assessing image
quality.
