TM-DIED: The Most Difficult Image Enhancement Dataset

As the name implies, this is an extended collection of 222 of my personal travel photos, constituting some of the most challenging cases for image enhancement and tone-mapping algorithms. Most of the photos include strong under/overexposed region, along with correctly exposed ones. The challenge is to automatically enhance these regions, without affecting the correctly exposed ones, and without any visible halo or other artifacts. If you want to check how your algorithm performs in real-life conditions, this the dataset to try.

Here are the details of the TM-DIED dataset:

• 222 high quality JPEG photos

• Full EXIF data (except from GPS)

• Many different cameras (point and shoot, mobile phones, DSLR)

• Different aspect ratios and sizes (e.g. panoramas, portrait, landscape)

• Both underexposed/overexposed and correctly exposed image regions

• Many different types of intensity transitions between under/overexposed and correctly exposed image regions.

• Different lighting conditions (night, sunset, day, cloudy, sunlight etc.)

• No visible identifiable faces (you can post them without issues of exposing someone's identity)

• Free for any type of research (academic or not)! Just mention the source :)

The dataset is hosted in Flickr and you can download it in the following link: https://www.flickr.com/gp/73847677@N02/GRn3G6

Also: Free Python code for processing this type of images is available in this repository.