
## Notes
* This model is a trained version of the Keras Tutorial [Image Super Resolution](https://keras.io/examples/vision/super_resolution_sub_pixel/) 
* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.


[Link to a pyimagesearch](https://www.pyimagesearch.com/2021/09/27/pixel-shuffle-super-resolution-with-tensorflow-keras-and-deep-learning/) tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net.
{"license": "mit", "tags": ["image-to-image"]}
keras image-to-image license:mit has_space region:us
TAGS
#keras #image-to-image #license-mit #has_space #region-us 


## Notes
* This model is a trained version of the Keras Tutorial Image Super Resolution 
* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.


Link to a pyimagesearch tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net.
## Notes
* This model is a trained version of the Keras Tutorial Image Super Resolution 
* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.


Link to a pyimagesearch tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net.
TAGS
#keras #image-to-image #license-mit #has_space #region-us 
 ## Notes
* This model is a trained version of the Keras Tutorial Image Super Resolution 
* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.


Link to a pyimagesearch tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net.
passage: TAGS
#keras #image-to-image #license-mit #has_space #region-us 
## Notes
* This model is a trained version of the Keras Tutorial Image Super Resolution 
* The model has been trained on inputs of dimension 100x100 and outputs images of 300x300.


Link to a pyimagesearch tutorial I worked on, where we have used Residual blocks along with the Efficient sub pixel net.