Challenge
The goal of this mini challenge is to increase the resolution of a single image (by four times). The data for this task comes from the DIV2K dataset [1]. For this challenge, we prepared a mini-dataset, which consists of 500 training and 80 validation pairs of images, where the HR images have 2K resolution and the LR images are downsampled four times.
For each LR image, algorithms will increase the resolution of the images. The quality of the output will be evaluated based on the PSNR between the output and HR images. The idea is to allow an algorithm to reveal more details imperceptible in the LR image.
Computational Resource
You can use the computational resources assigned by the MSAI course. Alternatively, you can use Amazon's EC2 or Google CoLab for computation. As a student, you can sign up to receive free $100 credit through the AWS Educate program. We encourage students to use g2.2xlarge instances running Ubuntu for maximal ease of installing. Note that $100 of Amazon credit allows you to run a g2.2xlarge GPU instance for approximately 6 days without interruption (you should keep it on only while using it).
References
[1] E. Agustsson and R. Timofte, NTIRE 2017 Challenge on Single Image Super-
Resolution: Dataset and Study, CVPRW 2017
[2] C. Ledig et al., Photo-realistic single image super-resolution using a generative adversarial network, CVPR17
[3] BasicSR: https://github.com/xinntao/BasicSR
[4] MMEditing: https://github.com/open-mmlab/mmediting
[5] He et al. Bag of Tricks for Image Classification with Convolutional Neural Networks, ArXiv 2018
