Given a short query with keyphrases about a research idea in 2019 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: I want to train a model for super-resolution.

Keyphrases: super-resolution images

Dataset description:
REDS: The realistic and dynamic scenes (REDS) dataset was proposed in the NTIRE19 Challenge. The dataset is composed of 300 video sequences with resolution of 720×1,280, and each video has 100 frames, where the training set, the validation set and the testing set have 240, 30 and 30 videos, respectively
SNIPS: The SNIPS Natural Language Understanding benchmark is a dataset of over 16,000 crowdsourced queries distributed among 7 user intents of various complexity:   SearchCreativeWork (e.g. Find me the I, Robot television show), GetWeather (e.g.
DRealSR: DRealSR establishes a Super Resolution (SR) benchmark with diverse real-world degradation processes, mitigating the limitations of conventional simulated image degradation. It has been collected from five DSLR cameras in natural scenes and cover indoor and outdoor scenes avoiding moving objects, e.g., advertising posters, plants, offices, buildings.
Set5: The Set5 dataset is a dataset consisting of 5 images (“baby”, “bird”, “butterfly”, “head”, “woman”) commonly used for testing performance of Image Super-Resolution models.

Options:- A: REDS  B: SNIPS  C: DRealSR  D: Set5
Set5