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SIDD Benchmark - RawRGB - PSNR | Kaggle
SIDD Benchmark - sRGB - PSNR | Kaggle
SIDD Benchmark - RawRGB - SSIM | Kaggle
SIDD Benchmark - sRGB - SSIM | Kaggle
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Abstract | Papers | Code | License | SIDD Small | Camera Pipeline | SIDD Medium | SIDD Full | SIDD Benchmark
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.
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.
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.
We provide a small version of the dataset that consists of 160 image pairs (noisy and ground-truth) representing 160 scene instances. These images can be used for training/learning purposes.
For each image, the following is provided:
We provide a simple and light-weight camera image processing pipeline implemented in MATLAB. This pipeline can be used to render the raw-RGB images into sRGB images with optional tone mapping.
We provide 80% (~24,000 images) of the dataset for training/learning purposes. The rest of the dataset is held for the benchmark.
Below, we provide links to 160 scene instances. For each scene instance, the following is provided:
Note: scene instances appearing in gray are held for benchmark.
Each image is stored in a directory with the name of the scene instance as follows:
[scene-instance-number]_[scene_number]_[smartphone-camera-code]_[ISO-level]_[shutter-speed]_[illuminant-temperature]_[illuminant-brightness-code]
where "smartphone-camera-code" is one of the following:
Bayer pattern for each camera: bayer_patterns.csv
Camera noise level functions (NLF) as extracted from DNG files: noise_level_functions.csv
DNG images corresponding to noisy raw-RGB images
The first four digits in each DNG archive should match the corresponding scene instance number
For example: "0001_DNG.zip" corresponds to "0001_001_S6_00100_00060_3200_L"
Text files containing all links to the files below that can be used for batch-downloading (e.g.,
using custom
scripts or download managers):
Mirror
1 | Mirror
2
A new version of SIDD Benchmark (SIDD+) was 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
The SIDD Benchmark consists of 40 images representing 40 scene instances. These images can be used to benchmark denoising methods.
For each image, the following is provided in one directory:
The PSNR and SSIM values are calculated only on 32 blocks of size 256 by 256 pixels. The block positions are provided in a file named "BenchmarkBlocks32.mat".
Follow the instructions in the Code_v/_ReadMe.txt file to
Results of the NTIRE 2019 Challenge on Real Image Denoising can be found in this paper.
The following tables show the benchmark results published in the paper.