Eugene Siow
commited on
Commit
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eea1706
1
Parent(s):
cd43d33
Add update to dataset Div2k reference.
Browse files- README.md +3 -4
- config.json +1 -2
README.md
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@@ -4,7 +4,7 @@ tags:
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- super-image
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- image-super-resolution
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datasets:
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-
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metrics:
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- pnsr
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- ssim
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@@ -14,7 +14,7 @@ MSRN model pre-trained on DIV2K (800 images training, augmented to 4000 images,
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The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling x2 and model upscaling x2.
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![Comparing Bicubic upscaling against the models
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## Model description
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The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
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config = MsrnConfig(
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scale=4, # train a model to upscale 4x
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bam=True, # apply balanced attention to the network
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supported_scales=[2, 3, 4],
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)
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model = MsrnModel(config)
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|Urban100 |3x | |**29.31/0.8737** |
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|Urban100 |4x |23.14/0.6573 |**26.10/0.7857** |
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## Model description
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The MSRN model proposes a feature extraction structure called the multi-scale residual block. This module can "adaptively detect image features at different scales" and "exploit the potential features of the image".
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config = MsrnConfig(
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scale=4, # train a model to upscale 4x
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bam=True, # apply balanced attention to the network
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)
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model = MsrnModel(config)
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|Urban100 |3x | |**29.31/0.8737** |
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|Urban100 |4x |23.14/0.6573 |**26.10/0.7857** |
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## BibTeX entry and citation info
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```bibtex
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config.json
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"bam": true,
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"n_feats": 64,
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"n_blocks": 8,
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"rgb_range": 255
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"supported_scales": [2,3,4]
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}
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"bam": true,
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"n_feats": 64,
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"n_blocks": 8,
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"rgb_range": 255
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}
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