Comments (2)
Your idea deserves a try. To be honest, I didn't try it because I believe image restoration is a very local problem. We should process the image pixel by pixel (attention between pixels). From my intuition, there should have a performance drop if we process them patch by patch (attention between patches such as what IPT does). Imagine that the patch embedding process (e.g., using a strided convolution layer) is not invertible. The spatial information within a patch is lost, which may have extreme burden for the model to reconstruct pixels (pixel is our final goal) given a patch feature.
However, it can reduce the computation burden if we do so. You can try to use it and see the results.
from swinir.
Feel free to open it if you have more questions.
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
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from swinir.