DeepISP: Learning End-to-End Image Processing Pipeline
Eli Schwartz, Raja Giryes and Alex Bronstein
Abstract
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.
Paper
Dataset
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For each scene there are 4 images ./id/medium_expusure.dng ./id/medium_expusure.jpg ./id/short_expusure1.dng ./id/short_expusure.jpg.
I also have more data not included - jpg and dng for longer exposure and 5 more short exposure dng images for each scene. Please contact me if you are intersted in this data. eli.shw at gmail.
