Row 12651

Row ID: 12651 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 12651 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Hello,

I'm working on a super-resolution project and I need some help.

The general steps I use in my project are:

1. Taking high-resolution (HR) images and downsampling them to low-resolution (LR) images by a factor of 2, then adding noise. 2. Upsampling the LR images using bicubic interpolation by a factor of 2. 3. Training: 1. Inserting the LR images into my model. 2. Calculating the loss between the results and HR images.

The problem arises when I test the model. If I upsample the images by a factor of 2 before testing the model, I get good results. However, when I test the model without upsampling the images beforehand, I get poor results as you can see. Why is that?

[Test by upsampling \(factor 2\) and then applying the model](https://preview.redd.it/bfsaa44l7m1d1.png?width=1570&format=png&auto=webp&s=b4b30e25d36c2bab49f545a7f1616e9389407003)

[Test by simply applying the model](https://preview.redd.it/kaxqksgm7m1d1.png?width=1570&format=png&auto=webp&s=6aa3d7e3bfbd127da0ed207556156cbd836c9a4a)

FieldValue
text Hello, I'm working on a super-resolution project and I need some help. The general steps I use in my project are: 1. Taking high-resolution (HR) images and downsampling them to low-resolution (LR) images by a factor of 2, then adding noise. 2. Upsampling the LR images using bicubic interpolation by a factor of 2. 3. Training: 1. Inserting the LR images into my model. 2. Calculating the loss between the results and HR images. The problem arises when I test the model. If I upsample the i…
label r/deeplearning
dataType post
communityName r/deeplearning
datetime 2024-05-20
username_encoded Z0FBQUFBQm5Lakw2Z3RoUjlZQzVYZmRuLWEzcnFaR1ljOTFYZ2hlSTl6cFlLWFFwUTVIcU9GUXR1cWRUakRkNkNVQU9oazVBRTFiZmxSMnZYNVpHeUtMSzdRSEJqaHV1YmdtRklzUVdRNVlCRExJTTFDNlAyNU09
url_encoded Z0FBQUFBQm5Lak9LVkV3ZkZ6Sktoc2kyVXFwX05CRXhHVWJIeHNfbldpUVFGeUtyXzVucGVDa0c2X29iajFpRUNPS0JkV19CY3RiMnE3d25YTUM3cURheG9zdjFSRG5ndkNTNnRWQ3RLZ0RPTm55anM4bU5WX2tSN2g3a3pQdEU5UEhmcUlhMEh1eU9KQVhLeDNBTmhfRUkza01HYnQ1X3QteV9DMXVWcGdRbEV4UmJzNUlQdHV2ZmNuMGFNMnItQ3VvY214ZVVhd2ZZ

Raw Record

{
  "text": "Hello,\n\nI'm working on a super-resolution project and I need some help.\n\nThe general steps I use in my project are:\n\n1. Taking high-resolution (HR) images and downsampling them to low-resolution (LR) images by a factor of 2, then adding noise.\n2. Upsampling the LR images using bicubic interpolation by a factor of 2.\n3. Training:\n   1. Inserting the LR images into my model.\n   2. Calculating the loss between the results and HR images.\n\nThe problem arises when I test the model. If I upsample the images by a factor of 2 before testing the model, I get good results. However, when I test the model without upsampling the images beforehand, I get poor results as you can see. Why is that?\n\n[Test by upsampling \\(factor 2\\) and then applying the model](https://preview.redd.it/bfsaa44l7m1d1.png?width=1570&format=png&auto=webp&s=b4b30e25d36c2bab49f545a7f1616e9389407003)\n\n\n\n[Test by simply applying the model](https://preview.redd.it/kaxqksgm7m1d1.png?width=1570&format=png&auto=webp&s=6aa3d7e3bfbd127da0ed207556156cbd836c9a4a)",
  "label": "r/deeplearning",
  "dataType": "post",
  "communityName": "r/deeplearning",
  "datetime": "2024-05-20",
  "username_encoded": "Z0FBQUFBQm5Lakw2Z3RoUjlZQzVYZmRuLWEzcnFaR1ljOTFYZ2hlSTl6cFlLWFFwUTVIcU9GUXR1cWRUakRkNkNVQU9oazVBRTFiZmxSMnZYNVpHeUtMSzdRSEJqaHV1YmdtRklzUVdRNVlCRExJTTFDNlAyNU09",
  "url_encoded": "Z0FBQUFBQm5Lak9LVkV3ZkZ6Sktoc2kyVXFwX05CRXhHVWJIeHNfbldpUVFGeUtyXzVucGVDa0c2X29iajFpRUNPS0JkV19CY3RiMnE3d25YTUM3cURheG9zdjFSRG5ndkNTNnRWQ3RLZ0RPTm55anM4bU5WX2tSN2g3a3pQdEU5UEhmcUlhMEh1eU9KQVhLeDNBTmhfRUkza01HYnQ1X3QteV9DMXVWcGdRbEV4UmJzNUlQdHV2ZmNuMGFNMnItQ3VvY214ZVVhd2ZZ"
}
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