Comments (5)
Possibly yes from my intuition. But we need experiments to prove it.
from swinir.
We mainly try to deal with the SR problem at the beginning. This is why we use such a design. UNet-like framework should also work. You can find similar ideas here, here and here.
from swinir.
We mainly try to deal with the SR problem at the beginning. This is why we use such a design. UNet-like framework should also work. You can find similar ideas here and here.
So do you mean that your framework is more suitable for the SR task than the Unet-like or autoencoder-like transformer-based framework?
Thanks.
from swinir.
Thanks very much for the patient reply.
from swinir.
Hi,
I now understand why this framework works well. Other tasks like segmentation or detection, need to extract the high-level semantic information. Thus the unet or autoencoder can make a contribution. Different from those tasks, SR is aimed at utilizing all the information from the LR image and we do not want to lose any low-level information. As such, your framework is intuitively better for SR.
from swinir.
Related Issues (20)
- Colab notebook error
- About self-ensemble strategy
- not compatible with the latest cog version
- Did you train SwinIR on DIV test set?
- How to disable using two GPUs for training?
- only 1 swin layer in the RSTB module?
- It seems SwinIR doesn't use patch merging. HOT 2
- Loading pretrained weight achiving not accurate result HOT 1
- Error(s) in loading state_dict for SwinIR HOT 5
- Inquiry about patch embedding HOT 4
- 关于X8的测试集
- JPEG Artifact Removal window size
- Transfer Learning with SWINIR model
- Artifact SWINIR (training Model as Generator GAN) HOT 1
- dynamic shape inference with onnx model HOT 1
- The noise removal command eats up my entire RAM and then gets killed HOT 5
- Load model takes forever
- SWINIR as Generator in GAN : Real world
- Unable to load pretrained model
- change the video card to run on the site replicate HOT 1
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from swinir.