The FilmGrain Dataset is a set of image pairs with and without film grain (grainy / grain-free) at five different film grain intensity levels.

It is intended to be used for training a deep learning model for film grain removal and synthesis, film grain detection, film grain intensity prediction, etc. A detailed description of the benchmark can be found on our Data 

. The license conditions are mentioned on the 

This Dataset comes from a collaboration with the IETR Lab (

All documents and papers that report on research that uses the FilmGrain Dataset must acknowledge the use of the dataset by including an appropriate citation to the following:

Z. Ameur, W. Hamidouche, E. François, M. Radosavljević, D. Menard, C.-H. Demarty. Deep-based Film Grain Removal and Synthesis. 

Title = { Deep-based Film Grain Removal and Synthesis },

Author ={Zoubida Ameur and Wassim Hamidouche and Edouard François and Milo\v{s} Radosavljevi\’{c} and Daniel M\’{e}nard and Claire-H\'{e}l\`{e}ne Demarty},

It would be highly appreciated if this use was shared with InterDigital, at this address: 

, with “FilmGrain” in the subject of your email.

The FilmGrain Dataset contains both grain-free and grainy images.

Grain-free images come from large and different image datasets including : KADIS-700k [1], Flickr2K [2], DIV2K [3], BSD [4] and Waterloo [5] datasets counting in total 148694 image samples. Images from these sets are collected from websites for sharing photos and videos paying special attention to the image quality, to the diversity of sources, to the image contents and to the copyrights.

Grainy images are obtained by adding film grain at five different intensities to the grain-free images. Film grain is added using the publicly available code [6] which consists in an implementation of a film grain rendering algorithm. A wide range of grain types and intensities can be generated by varying the parameters of the algorithm, among which one main parameter is the average grain radius µr.

The grainy images are obtained by adding film grain at five different intensities by varying the average grain radius μr in {0.010, 0.025, 0.050, 0.075, 0.100}.

Lin, Hanhe, Vlad Hosu, and Dietmar Saupe. "KADID-10k: A large-scale artificially distorted IQA database." 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX). IEEE, 2019.

Lim, Bee, et al. "Enhanced deep residual networks for single image super-resolution." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017.

Agustsson, Eirikur, and Radu Timofte. "Ntire 2017 challenge on single image super-resolution: Dataset and study." Proceedings of the IEEE conference on computer vision and pattern recognition workshops. 2017.

Martin, David, et al. "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics." Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001. Vol. 2. IEEE, 2001.

Ma, Kede, et al. "Waterloo exploration database: New challenges for image quality assessment models." IEEE Transactions on Image Processing 26.2 (2016): 1004-1016.

Newson, Alasdair, Julie Delon, and Bruno Galerne. "A Stochastic Film Grain Model for Resolution‐Independent Rendering." Computer Graphics Forum. Vol. 36. No. 8. 2017.

In order to get the data, you are asked to supply your 

name, affiliation, email address and intended use of the dataset.

You will receive instructions on how to download the dataset via this email address. We may store the data you supplied in order to contact you later about benchmark related matters. The data will not be used in any other way.

To download the FilmGrain Dataset please send an email to filmgrainmanagement@interdigital.com. By doing this you irrevocably agree to any and all provision of the license agreement on this page.

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Their corresponding grainy versions for 5 different intensity levels.

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