Smartphone Image Denoising Dataset

Abdelrahman Abdelhamed1             Stephen Lin2             Michael S. Brown1

1York University             2Microsoft Research

Small Dataset

Browse

Download

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

[sRGB images only (~6 GB)]   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)]   MD5: 0f44ddb6ec820271c9996aa32a9cc270   SHA1: 886259b1c5e139dbd05a5d02cbca526b12849825

[sRGB images only (~12 GB)]   MD5: f95b4bc9ec1dd3fe4ebd61aeacad3991   SHA1: b0f895258112db896d6ade0a8ddafc8cfc9bd54d

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

Download

SIDD_URLs.txt: a text file containing all links to the files below that can be used for batch-downloading (e.g., using custom scripts or download managers).


0001_001_S6_00100_00060_3200_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0002_001_S6_00100_00020_3200_N
Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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0003_001_S6_00100_00060_3200_H
Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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0004_001_S6_00100_00060_4400_L
Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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0005_001_S6_00100_00060_4400_N
Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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0006_001_S6_00100_00060_4400_H
Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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0008_001_S6_00100_00100_5500_N
Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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0009_001_S6_00800_00350_3200_L
Held for benchmark
0010_001_S6_00800_00350_3200_N
Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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0011_001_S6_00800_00500_5500_L
Noisy Raw-RGB
GT Raw-RGB
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GT sRGB
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Noisy Raw-RGB
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Noisy Raw-RGB
GT Raw-RGB
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Noisy Raw-RGB
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Noisy Raw-RGB
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0021_001_GP_10000_05000_5500_N
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Noisy Raw-RGB
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Noisy Raw-RGB
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Held for benchmark
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Noisy Raw-RGB
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Held for benchmark
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GT Raw-RGB
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0042_002_IP_01600_03100_5500_N
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0051_002_S6_00100_00060_5500_N
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0054_003_N6_00100_00160_5500_N
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0063_003_GP_00100_00100_4400_N
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0080_004_S6_00200_00050_3200_N
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0083_004_GP_00050_00020_4400_N
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Noisy Raw-RGB
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0086_004_GP_00100_00100_5500_L
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GT sRGB
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0093_004_IP_01250_00250_3200_L
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0094_005_N6_00100_00050_3200_L
Noisy Raw-RGB
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Held for benchmark
0096_005_N6_01600_01000_3200_L
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0097_005_N6_03200_02000_3200_L
Noisy Raw-RGB
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Noisy Raw-RGB
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0100_005_G4_00800_00400_3200_N
Held for benchmark
0101_005_S6_00100_00050_4400_L
Noisy Raw-RGB
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0104_005_S6_03200_01600_4400_L
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0108_005_GP_06400_06400_4400_N
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0110_005_IP_00100_00100_5500_L
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0129_006_GP_00100_00100_4400_N
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0144_007_N6_01600_01600_4400_N
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0151_007_S6_00800_00800_5500_L
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0152_007_S6_01600_01600_5500_L
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0153_007_S6_03200_03200_5500_L
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0154_007_S6_00400_00400_5500_L
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0155_007_GP_00100_00100_5500_N
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0157_007_GP_01600_01600_5500_N
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0158_007_GP_03200_03200_5500_N
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0159_007_IP_00100_00100_3200_L
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0161_007_IP_00800_00800_3200_L
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0163_007_IP_00100_00100_3200_N
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GT sRGB
Metadata
0173_008_G4_00400_00400_4400_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0174_008_G4_00800_00800_4400_N
Held for benchmark
0175_008_S6_00100_00025_5500_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0176_008_S6_00400_00100_5500_L
Held for benchmark
0177_008_S6_00800_00200_5500_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0178_008_S6_01600_00400_5500_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0179_008_S6_03200_00800_5500_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0180_008_GP_00100_00100_5500_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0181_008_GP_00800_00800_5500_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0182_008_GP_03200_03200_5500_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0183_008_GP_06400_06400_5500_N
Held for benchmark
0184_008_IP_00100_00100_3200_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0185_008_IP_00400_00400_3200_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0186_008_IP_00800_00800_3200_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0187_008_IP_01600_01600_3200_L
Held for benchmark
0188_008_IP_00100_00100_3200_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0189_008_IP_00400_00400_3200_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0190_008_IP_00800_00800_3200_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0191_008_IP_01600_01600_3200_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0192_009_IP_00100_00200_3200_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0193_009_IP_00800_02000_3200_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0194_009_IP_01600_04000_3200_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0195_009_IP_01600_04000_5500_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0196_009_IP_00800_02000_5500_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0197_009_IP_00100_00200_5500_L
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0198_010_GP_00100_00200_5500_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0199_010_GP_00800_01600_5500_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata
0200_010_GP_01600_03200_5500_N
Noisy Raw-RGB
GT Raw-RGB
Noisy sRGB
GT sRGB
Metadata