- Implementations of SRDRM and SRDRM-GAN for underwater image super-resolution
- Simplified implementation of SRGAN, ESRGAN, EDSRGAN, ResNetSR, SRCNN, and DSRCNN
- Implementation: TensorFlow >= 1.11.0, Keras >= 2.2, and Python 2.7
Single Image Super-Resolution (SISR) | Color and sharpness |
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- Paper: soon
- USR-248 dataset: http://irvlab.cs.umn.edu/resources/usr-248-dataset
- Bibliography entry for citation:
article{islam2019fast, title={Underwater Image Super-Resolution using Deep Residual Multipliers}, author={Islam, Md Jahidul and Enan, Sadman Sakib and Luo, Peigen and Sattar, Junaed}, journal={arXiv preprint arXiv:x.y}, year={2019} }
- Video demo: https://youtu.be/qOLZVgrxCwE
2x SISR performance | 4x SISR performance |
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- Download the data, setup data-paths in the training scripts
- Use the individual scripts for training 2x, 4x, 8x SISR models
- train-GAN-nx.py: SRDRM-GAN, SRGAN, ESRGAN, EDSRGAN
- train-generative-models-nx.py: SRDRM, ResNetSR, SRCNN, DSRCNN
- Checkpoints: checkpoints/dataset-name/model-name/
- Samples: images/dataset-name/model-name/
- Use the test-scripts for evaluating different models
- A few test images: data/test/ (ground-truth: high_res)
- Output: data/output/
- A few saved models are provided in checkpoints/saved/
- Use the measure.py for quantitative analysis based on UIQM, SSIM, and PSNR
- Trade-offs between performance and running time. Requirements:
- Running time >= 5 FPS on Jetson-TX2
- Model size <= 12MB (no quantization)
- Challenges
- Performance for 8x models
- Inconsistent coloring, infrequent noisy patches
- https://github.com/wandb/superres
- https://github.com/david-gpu/srez
- https://github.com/Mulns/SuperSR
- https://github.com/tensorlayer/srgan
- https://github.com/icpm/super-resolution
- https://github.com/alexjc/neural-enhance
- https://github.com/jiny2001/dcscn-super-resolution
- https://github.com/titu1994/Image-Super-Resolution
- https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras