Smartphone Image Denoising Dataset

Abdelrahman Abdelhamed1             Stephen Lin2             Michael S. Brown1

1York University             2Microsoft Research

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A new version of SIDD Benchmark (SIDD+) is being hosted as a challenge at the New Trends in Image Restoration and Enhancement (NTIRE 2020) workshop in conjunction with CVPR 2020.
The participating solutions and results will be published in the challenge report in the CVPR 2020 Workshop proceedings.

Challenges can be accessed at the following Codalab competitions:
NTIRE 2020 Real Image Denoising Challenge - Track 1 - rawRGB
NTIRE 2020 Real Image Denoising Challenge - Track 2 - sRGB

Noisy Image Noisy
Ground Truth Image Ground Truth


The last decade has seen an astronomical shift from imaging with DSLR and point-and-shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor size, smartphone images have notably more noise than their DSLR counterparts. While denoising for smartphone images is an active research area, the research community currently lacks a denoising image dataset representative of real noisy images from smartphone cameras with high-quality ground truth. We address this issue in this paper with the following contributions. We propose a systematic procedure for estimating ground truth for noisy images that can be used to benchmark denoising performance for smartphone cameras. Using this procedure, we have captured a dataset, the Smartphone Image Denoising Dataset (SIDD), of ~30,000 noisy images from 10 scenes under different lighting conditions using five representative smartphone cameras and generated their ground truth images. We used this dataset to benchmark a number of denoising algorithms. We show that CNN-based methods perform better when trained on our high-quality dataset than when trained using alternative strategies, such as low-ISO images used as a proxy for ground truth data.


Abdelrahman Abdelhamed, Lin S., Brown M. S. "A High-Quality Denoising Dataset for Smartphone Cameras", IEEE Computer Vision and Pattern Recognition (CVPR), June 2018.

[PDF]   [Bibtex]

Abdelrahman Abdelhamed, Timofte R., Brown M. S., et al. "NTIRE 2019 Challenge on Real Image Denoising: Methods and Results", IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), June 2019.

[PDF]   [Bibtex]


Ground-truth image estimation

A simple camera pipeline for rendering raw-RGB images into sRGB.


The dataset and the associated code repositories are under the MIT License.


For any questions, remarks, or comments, please contact: Abdelrahman Abdelhamed.