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--- |
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license: cc-by-4.0 |
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tags: |
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- super-resolution |
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size_categories: |
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- 100K<n<1M |
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--- |
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# BHI_LR_multi |
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This is a x4 LR counterpart to the [BHI SISR Dataset](https://huggingface.co/datasets/Phips/BHI/blob/main/README.md). |
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**SCREENSHOT HERE** |
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To create this dataset, a 0.25 scaling (or x4 scaling) has been applied, with multiple scaling algorithms in a randomized manner. |
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The algos used are linear, cubic_mitchell, lanczos, gauss, box and down_up (with these algos). |
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The specifically applied degradation for each image can be found in the [applied_degradations.txt](https://huggingface.co/datasets/Phips/BHI_LR_multi/resolve/main/applied_degradations.txt?download=true) file |
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The trained model learns to handle multiple scalings, and also these counteract each other in the sense of that lanczos will always sharpen, gauss will always soften etc. |
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Using this instead of just bicubic sampling for example will help the model not to be trained on / pick up on one specific scaling algos characteristics. |
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<figure> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/634e9aa407e669188d3912f9/_vs_h4eygsFC27qRdhVha.png" alt="screenshot beginning of applied_degradations.txt"> |
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<figcaption>Applied scaling on each image, screenshot beginning of applied_degradations.txt</figcaption> |
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</figure> |
