Image Enhancement

Enhancing an image means modifying one or more of its visual features in order to increase the visibility of its content and enable its understanding. This is very important both in human as well as 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.

Image Enhancement Datasets

The page lists some datasets of real-world 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 stored as .tgz archives. You may use gunzip and tar to unzip them.


  • Personal-DB: This dataset contains 55 real-world color images along with their enhancement results achieved by the algorithm SuPeR-B.[6, 8] for different values of its parameters. The input images and their enhanced versions are arranged in 'panels', where the first image is the input one and the others are its enhanced versions. The input images have been captured by standard cameras and depict indoor and outdoor environments with backlight, spotlight and colored light. In [8], the dataset has been used to test SuPeR-B by means of objective and subjective measures. Please cite [8] when using this dataset.

  • SCA-30 - This data-set 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 data-set adequate also for testing and comparing image enhancers having a very high computational complexity.

[Size of compressed archive: 2.4 M]

The dataset SCA-30 has been used to this purpose in the paper [5]:

G. Simone, M. Lecca, G. Gianini, A. Rizzi, A Survey of Methods and Evaluation of Retinex-inspired Image Enhancers, to appear on JEI, 2022

The original images of SCA-30 and their versions enhanced by 35 Retinex-inspired enhancers are available here (see the sub directories Original and Filtered contained in the zipped archive).

[Size of compressed archive: 92.1 M]

Data and plots discussed in the paper above are downloadable here.

[Size of compressed archive: 7.7 M]

  • Pict20 - This data-set contains 20 images in jpeg format, named 001.jpeg, …., 020.jpeg. Their size ranges from 123M to 335M pixels. The dataset also includes some images from SCA-30 but captured with higher resolution.

[Size of compressed archive: 616 K]

  • Pgshutter - This data-set consists of 20 images, acquired by the FLIR camera with auto-exposure off. Most images, that have size 640 x 480, are very dark. This makes the data-set adequate to evaluate image enhancement under low light conditions.

[Size of compressed archive: 324 KB]

  • MEXICO (Multi-EXposure Image COllection) - This data-set 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 jpeg format, are organized in 40 folders: folder X contains the 10 pictures of 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 data-set MEXICO has been used in ref. [1]. The multi-exposure times (corresponding to under-, well-, over- exposed images) enables researchers 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.

  • MEXICO-gr20 - This data-set is a subset of the data-set 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 data-set 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 data-set is 4,5 MB, the downloadable archive requires 3,0 MB of space.

  • MEXICO-2020 (Multi-EXposure Image COllection - 2020) - This data-set is an extended version of the data-set MEXICO. It contains 50 scenes of indoor and outdoor environments, each captured at different exposure times (T = 6, 15, 18, 21, 24, and 27 ms) with a FLIR camera FMVU-03MTM-CS. The data-set includes most of the images from MEXICO. The names of the images are like those in MEXICO. The data-set has been used in refs. [2, 3, 4].

The size of the data-set downloadable archive is 7,5 MB. The data-set size is 8,12 MB.

images from the dataset

MEXICO example - shutter speed from 3 ms to 30 ms


Along with the datasets, we provide also the following software, that is provided as a .tgz archive along with license and instructions:

  • GREAT-v2019: an implementation of the spatial color algorithm GREAT[1] [2] as a binary executable file running on Windows 10 x64.


  1. M. Lecca, A. Rizzi, R.P. Serapioni, GREAT: a gradient-based color-sampling scheme for Retinex, Journal of the Optical Society of America. A - Optics, Image Science, and Vision, vol. 34, n. 4, pp. 513-522, 2017

  2. M. Lecca, Michela, A. Torresani, F. Remondino, On Image Enhancement for Unsupervised Image Description and Matching, International Conference on Image Analysis and Processing - ICIAP, LNCS vol.11752, n. Part II, pp. 82-92, 2019

  3. M. Lecca, A. Torresani, F. Remondino. Comprehensive evaluation of image enhancement for unsupervised image description and matching. IET Image Processing 14.16 (2020): 4329-4339

  4. M. Lecca, F. Poiesi, Performance comparison of image enhancers with and without deep learning, Journal of Optical Society of America A, Vol. 39, 610-620 (2022)

  5. G. Simone, M. Lecca, G. Gianini, A. Rizzi, A Survey of Methods and Evaluation of Retinex-inspired Image Enhancers, J. of Electronic Imaging, to appear (2022).

  6. M. Lecca, "A Retinex Inspired Bilateral Filter for Enhancing Images under Difficult Light Conditions." VISIGRAPP (4: VISAPP). Online Streaming, 2021,

  7. M. Lecca. Underwater image enhancement by the Retinex inspired Contrast Enhancer STRESS, 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - VISAPP, Vol 4, 2022

  8. M. Lecca, Enhancing Backlight and Spotlight Images by the Retinex - inspired bilateral filter SuPeR-B, to appear 2023