DeepISP: Towards Learning an End-to-End Image Processing Pipeline
This addresses the need for improved image processing in smartphone cameras, though it is incremental as it builds on existing deep learning methods for ISP tasks.
The authors tackled the problem of creating an end-to-end image processing pipeline for cameras by developing DeepISP, which maps raw low-light images to visually compelling outputs, achieving state-of-the-art PSNR in joint denoising and demosaicing and better visual quality than manufacturer ISPs.
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.