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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- code |
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pretty_name: upscaler |
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size_categories: |
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- 100K<n<1M |
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task_categories: |
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- image-to-image |
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--- |
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# Dataset Card for Latent Diffusion Super Sampling |
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Image datasets for building image/video upscaling networks. |
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This repository contains implementation of training and inference code for models trained on the following works: |
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## Part 1: Trained Sub-Pixel Convolutional Network for Upscaling on 5000 individual 720p-4K and 1080p-4K image pairs |
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### References: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network, Shi et al |
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Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D. and Wang, Z., 2016. |
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### Results: |
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720p images tested: 100 <br> |
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Average PSNR: 40.44 dB <br> |
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1080p images tested: 100 <br> |
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Average PSNR: 43.05 dB <br> |
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This outperforms the model in the proposed architecture (average is 28.09 dB) <br> |
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Refer points 5,6,7 in Section "Dataset Description" for further information <br> |
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## Part 2: Trained Convolutional Neural Network Video Frame Interpolation via Spatially-adaptive Separable Convolution for realtime 4K, 1080p and 720p videos |
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### References: Video Frame Interpolation via Adaptive Separable Convolution, Niklaus et al |
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sniklaus@pdx.edu, mtlong@cs.pdx.edu, fliu@cs.pdx.edu |
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### Results: |
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720p Model: <br> |
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•PSNR: 28.35 <br> |
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•SSIM: 0.78 <br> |
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1080p Model: <br> |
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•PSNR: 29.67 <br> |
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•SSIM: 0.84 <br> |
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4K Model: <br> |
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•PSNR: 33.74 <br> |
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•SSIM: 0.83 <br> |
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Refer points 8,9,10 in Section "Dataset Description" for further information <br> |
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## Part 3: Latent Diffusion Super Sampling coming soon!!! |
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Stay tuned>>>>>>>>>>>>>>> |
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## Dataset Details |
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Consists of 300,000 ground truth 720p and 1080p frames with corresponding 4K output frames |
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### Dataset Description |
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1. 4K_part1: Contains first part of 4K images |
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2. 4K_part2: Contains second part of 4K images |
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3. 720p: Contains 100,000 ground truth 720p images |
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4. 1080p: Contains 100,000 ground truth 1080p images |
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5. Additionally, you will find 2 ESPCN (Efficient Sub Pixel Convolution Network) PyTorch models and a Jupyter Notebook (ESPCN.ipynb), which you can use for retraining or inference. |
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6. Selected Super Resolution 5000 contains 5000 randomly picked image triplets for 4K, 1080p and 720p images. |
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7. Super Resolution Test 100 serves as the test dataset for the above training set. |
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8. In the latest update, 3 FIASC (Frame Interpolation via Adaptive Separable Convolutional) PyTorch models and a Jupyter Notebook (FIASC.ipynb) have been added to be used for retraining or inference. |
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9. Frame Interpolation Training contains 6416 frames used for training the models, each respectively for 4K, 1080p and 720p. |
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10. Frame Interpolation Testing contains 1309 frames used for evaluating the models, each respectively for 4K, 1080p and 720p. |
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### Dataset Sources |
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YouTube |
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## Uses |
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Diffusion networks, CNNs, Optical Flow Accelerators, etc. |
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## Dataset Structure |
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1. All images are in .jpg format |
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2. Images are named in the following format: *resolution_globalframenumber.jpg* |
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3. Resolution refers to either of 3: *720p, 1080p or 4K* |
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4. Globalframenumber is the frame number of the image under the respective resolution. *eg: 4K_10090.jpg* |
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### Curation Rationale |
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1. To build a real-time upscaling network using latent diffusion supersampling. |
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2. Design algorithms for increasing temporal resolution (framerate up-conversion) of videos in real-time. |
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## Dataset Card Authors |
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Alosh Denny |
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## Dataset Card Contact |
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aloshdenny@gmail.com |
