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Image Enhancement Datasets and Software
Enhancing an image means modifying one or more of its visual features in order to increase the visibility of its content and to enable its understanding. This is very important in both human and machine vision. Algorithms removing or lowering noise, shadows, artifacts or color casts due to the illumination under which an image has been acquired are highly relevant to a plethora of applications, including for instance robust object/people detection and tracking, analysis of medical images, image rendering for entertainment and augmented reality, 3D reconstruction.
The page lists some datasets of real-world color images, acquired by different devices, in indoor and outdoor environments, under different light conditions, including low-light and back-light. Many images are characterized by the co-existence of bright and dark regions with different size and proportion. These features make these datasets particularly appropriate to test image enhancers based on spatial color analysis.
Datasets are downloadable as .tgz archives. You may use gunzip and tar to unzip them.
SCA-30 - this dataset includes 30 pictures in bmp format, named sca-30 (1).bmp, …, sca-30 (30).bmp. These images have low resolution: their size was set up on average to 44M in order to make the dataset adequate also for testing and comparing image enhancers having a very high computational complexity. [Size of compressed archive: 2.4 MB]
Pict20 - this dataset contains 20 images in jpeg format, named 001.jpeg, …., 020.jpeg. Their size range from 123M to 335M pixels about. The dataset includes also some images from SCA-30 but captured with higher resolution. [Size of compressed archive: 616 KB]
Pgshutter - this dataset consists of 20 images, acquired by the FLIR camera with auto-exposure off. Most of images, that have size 640 x 480, are very dark. This makes the dataset adequate to evaluate image enhancement under low light conditions. [Size of compressed archive: 324 KB]
- MEXICO (Multi-EXposure Image COllection) - this dataset contains 400 color pictures with size 640 x 480 depicting 40 scenes of real-world indoor or outdoor environments characterized by challenging issues for image enhancement, as e.g. the co-existence of dark and bright regions at different proportions, back-light, shadows, chromatic dominants of the illuminant, presence regions with different granularity, from uniform to highly textured. Each scene has been captured with 10 different exposure times (from 3 to 30 ms with step of 3ms) by the FLIR Camera. The images, which are saved in jpg format, are organized in 40 folders: the folder X contains the 10 pictures of the scene X. The images of each folder are named Img_Shutter_Y.jpeg, where Y codifies the exposure time at which the image has been acquired (Y = 03, 06, …, 30 ms). The dataset MEXICO has been used in .
- MEXICO-gr20 - This dataset is a subset of the dataset MEXICO including 60 images. MEXICO-gr20 includes 20 scenes, each of which represents an indoor or outdoor environment acquired with the FireFly camera under the three exposure times 6 ms, 21 ms, 30 ms. These images, originally acquired as color pictures and with size 640 x 480, have been half-scaled and converted to gray-level. The suffix “gr20” in the dataset name is just an acronym reminding that the images are in gray-level (“g”), re-sized (“r”) and their number is 20. Each image is named ‘sceneX-Y.pgm” where X identifies the corresponding scene and Y codifies its exposure time (Y = 06, 21, 30).
The size of the dataset is 4,5 MB, the archive down-loadable here requires 3,0 MB of space.
The purpose of the dataset is to allow the tests of algorithms working on a single image channel and characterized by high complexity and long execution time, such as spatial color algorithms or image descriptors processing the image pixel-by-pixel, The multi-exposure times (corresponding to under-, well-, over- exposed images) enables to investigate the robustness of image processing methods to light dimming (thus to shadows or changes of the intensity of the light of the acquired scene), and to test HDR methods as well.
Along with the datasets, we provide also the following software, that is downloadable as a .tgz archive along with license and instructions:
GREAT-v2019 - an implementation of the spatial color algorithm GREAT  as a binary executable file running on Windows 10 x64.
 Lecca, Michela; Torresani, Alessandro; Remondino, Fabio, On Image Enhancement for Unsupervised Image Description and Matching, Proceedings of International Conference on Image Analysis and Processing (ICIAP), Springer, vol.11752, n. Part II, 2019, pp. 82-92, (ICIAP 2019, Trento, Italy, 9-13 Sept. 2019)
 Lecca, Michela; Rizzi, Alessandro; Serapioni, Raul P., GREAT: a gradient-based color-sampling scheme for Retinex, in «JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION», vol. 34, n. 4, 2017, pp. 513-522