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.

DeepISP 是一个全面的端到端深度神经模型，用于照相机图像信号处理（ISP）管道，学习从原始低光马赛克图像到最终具有视觉吸引力的图像的映射，并包括去马赛克、降噪以及色彩校正等低层次任务，以及更高层次的图像调整任务。该解决方案在 PSNR 的客观评估方面实现了最先进的性能，在完整的端到端管道中，相比制造商 ISP，在主观人类评估和由用于评估图像质量的深度模型评分时具有更好的视觉质量。