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README.md
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@@ -13,7 +13,8 @@ To create this dataset, a 0.25 scaling (or x4 scaling) has been applied, with mu
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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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<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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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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