Smartphone Image Denoising Dataset

Abdelrahman Abdelhamed1             Stephen Lin2             Michael S. Brown1

1York University             2Microsoft Research

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Noisy Image Noisy
Ground Truth Image Ground Truth

Abstract | Papers | Code | License | SIDD Small | Camera Pipeline | SIDD Medium | SIDD Full | SIDD Benchmark

Abstract

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.

Papers

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]

Code

Ground-truth image estimation

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

License

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

Contact

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

SIDD-Small Dataset

Download

Raw-RGB images only (~10 GB) [ Mirror 1 | Mirror 2 ]
MD5: c033f580fb64aa549679d056d1cf796a   SHA1: 932c39f4fc5410dda934a3493ddb46c31b8a5e90

sRGB images only (~6 GB) [ Mirror 1 | Mirror 2 ]
MD5: 796971867583bf14677dcae510e52538   SHA1: 5a4aa6aa7abcf7b0b56c88ad578fea9a4ff77935

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:

  1. Noisy Raw-RGB image (.MAT).
  2. Ground truth Raw-RGB image (.MAT).
  3. Noisy sRGB image (.PNG).
  4. Ground truth sRGB image (.PNG).
  5. Metadata extracted from the DNG file (.MAT).

Camera Pipeline (Rendering from raw-RGB to sRGB)

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.

[ GitHub | MathWorks ]

SIDD-Medium Dataset

The SIDD Medium dataset is similar to SIDD Small dataset excpet that SIDD Medium consists of 320 image pairs (noisy and ground-truth), two image pairs from each scene instance.

Download

Raw-RGB images only (~20 GB)
Mirror 1
Mirror 2 (parts): [ Part 0 | Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 6 | Part 7 | Part 8 | Part 9 | Part 10 | Part 11 | Part 12 | Part 13 | Part 14 | Part 15 | Part 16 | Part 17 | Part 18 | Part 19 | Part 20 ]
To unzip the file parts, run:
zip -FF SIDD_Medium_Srgb_Parts.zip --out combined.zip
unzip combined.zip

MD5: 0f44ddb6ec820271c9996aa32a9cc270   SHA1: 886259b1c5e139dbd05a5d02cbca526b12849825

sRGB images only (~12 GB)
Mirror 1
Mirror 2 (parts): [ Part 0 | Part 1 | Part 2 | Part 3 | Part 4 | Part 5 | Part 6 | Part 7 | Part 8 | Part 9 | Part 10 | Part 11 | Part 12 ]
To unzip the file parts, run:
zip -FF SIDD_Medium_Srgb_Parts.zip --out combined.zip
unzip combined.zip

MD5: f95b4bc9ec1dd3fe4ebd61aeacad3991   SHA1: b0f895258112db896d6ade0a8ddafc8cfc9bd54d

SIDD-Full Dataset

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:

  1. 150 noisy Raw-RGB image (.MAT).
  2. 150 ground truth Raw-RGB image (.MAT).
  3. 150 noisy sRGB image (.PNG).
  4. 150 ground truth sRGB image (.PNG).
  5. 150 metadata extracted from the DNG file (.MAT).

Note: scene instances appearing in gray are held for benchmark.

Image directory naming convention

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:

and "illuminant-brightness-code" is one of the following: For example, the following directory name:
0052_002_S6_01600_01000_5500_N
means that this is scene instance 52, from scene 2, captured be Samsung Galaxy S6 Edge (S6), using ISO level of 1600, using shutter speed of 1000 (i.e., exposure time of 1/1000 second), under illuminant temperature of 5500K, under normal (N) brightness.

Useful metadata

Bayer pattern for each camera: bayer_patterns.csv

Camera noise level functions (NLF) as extracted from DNG files: noise_level_functions.csv

DNG images

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"

Download

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

Download individual scene instances

SIDD Benchmark

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

Download

SIDD Benchmark Code v1.2 (9 KB) [ Mirror 1 | Mirror 2 | Mirror 3 | Mirror 4 ]

SIDD Benchmark Data as single .mat arrays of dimensoins [#images, #blocks, height, width, #channels]:
Noisy raw-RGB data: [ Mirror 1 | Mirror 2 ]
Noisy sRGB data: [ Mirror 1 | Mirror 2 ]

SIDD Validation Data and Ground Truth as single .mat arrays of dimensoins [#images, #blocks, height, width, #channels]:
Noisy raw-RGB data: [ Mirror 1 | Mirror 2 ]
Noisy sRGB data: [ Mirror 1 | Mirror 2 ]
Ground-truth raw-RGB data: [ Mirror 1 | Mirror 2 ]
Ground-truth sRGB data: [ Mirror 1 | Mirror 2 ]

SIDD Benchmark Data (full-frame images, 1.84 GB) [ Mirror 1 | Mirror 2 | Mirror 3 | Mirror 4 ]
MD5: decd113eaf99a8dbd1dbb7f7c9dafedd   SHA1: b8092d990139f41b6da97b4afa679a2876de53bd

Description

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:

  1. Noisy Raw-RGB image (.MAT). Black Level subtracted, normalized to [0, 1].
  2. Noisy sRGB image (.PNG). Gamma corrected, without any tone mapping.
  3. Metadata extracted from the DNG file (.MAT). For example, black and saturation levels, as-shot neutral, noise level function, etc.

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

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Benchmark Results

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.

Results of denoising in raw-RGB space

Raw-RGB results

Results of denoising in sRGB space

sRGB results