Image-super-resolution using Enhanced Deep Residual Networks for Single Image Super-Resolution(EDSR) and Wide Activation for Efficient and Accurate Image Super-Resolution(WDSR)

Image Super Resolution using EDSR and WDSR research papers

You can find Deployed Working model here

Find detailed blog of my project here

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Demo of Working Model

Proposed networks. We remove the batch normalization layers from our network as Nah et al presented in their image deblurring work. Since batch normalization layers normalize the features, they get rid of range flexibility from networks by normalizing the features, it is better to remove them.
Furthermore, GPU memory usage is also sufficiently reduced since the batch normalization layers consume the same amount of memory as the preceding convolutional layers. this baseline model without batch normalization layer saves approximately 40% of memory usage during training, compared to SRResNet. Consequently, we can build up a larger model that has better performance than conventional ResNet structure under limited computational resources.

DIV2K dataset is a newly proposed high-quality (2K resolution) image dataset for image restoration tasks. The DIV2K dataset consists of 800 training images, 100 validation images, and 100 test images. As the test dataset ground truth is not released, we report and compare the performances on the validation dataset. We also compare the performance on some of the standard benchmark datasets named Set5 and Set14.

Name		Name	Last commit message	Last commit date
Latest commit History 22 Commits
Architecture		Architecture
predicted_images		predicted_images
CS2_EDA.ipynb		CS2_EDA.ipynb
EDSR.ipynb		EDSR.ipynb
README.md		README.md
WDSR.ipynb		WDSR.ipynb
WDSR_bicubic_x4.ipynb		WDSR_bicubic_x4.ipynb
WDSR_unknown_x4.ipynb		WDSR_unknown_x4.ipynb
final.ipynb		final.ipynb
models.py		models.py
working_demo.gif		working_demo.gif