Dataset for Simultaneous Enhancement and Super-Resolution (SESR) of underwater imagery.

Introduced in this paper:  http://www.roboticsproceedings.org/rss16/p018.pdf (Pre-print)

Code repository: https://github.com/xahidbuffon/Deep-SESR

Project page: image-enhancement-and-super-resolution/deep-sesr

Folder structure:

• train_val/ contains 1500 paired samples for training/validation
• TEST/ contains 120 paired samples for benchmark evaluation

Paired data for each set:    

• hr/ contains high-resolution ground truth images: Y
• mask/ contains ground truth saliency maps: S
• lrd/ contains low-resolution distorted images: X  

Possible Usage:    

• Train SESR models: {X} → {Y}
• Train image enhancement models: {X} → {Y} (downscaled)
• Train image super-resolution models: {Y} (downscaled) → {Y}
• Train saliency prediction models: {Y} (downscaled) → {S}

The Y images are of size 640 x 480. The X and S are of size 320 x 240. Hence, the default configuration is for training 2x models; rescale X and S for other configurations: 3x, 4x, etc.