Once merged😎 , build a script for the same task in the img_super_res directory __(If you are using Deep Learning, ensure that you have saved your trained model and its weights so that in the script you build can simply fetch it instead of training again)
Use argparse library so that the input image and the output path can be given as arguments in the terminal while running the script
Update the requirements.txt file in the root directory of the master branch to ensure any additional modules you have used in present there.
Make sure you provide sample images/videos 📷 used
Task:
Take in a low very low-resolution image and output an enlarged and high resolution image
Suggested workflow:
deeppixel
directory, create a new sub-directoryimg_super_res
__[Please name it appropriately and use camel_case]__Developed Jupyter Notebook for Image Super-Resolution
, briefing about your approach in the description and add a link of the above notebook in Google Colab [Please ensure you have given access] â›”img_super_res
directory __(If you are using Deep Learning, ensure that you have saved your trained model and its weights so that in the script you build can simply fetch it instead of training again)requirements.txt
file in the root directory of the master branch to ensure any additional modules you have used in present there.Developed Script for Image Super-Resolution
and mention how you have given the argument parameters to run the script in the descriptionREADME.MD
file with appropriate description [Please ensure you properly cite any research paper or blog you have taken direct reference from]Documentation Updated for Image Super-Resolution
References :
Will be updated with more soon, Meanwhile, feel free to try these
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Second-Order Attention Network for Single Image Super-Resolution
Image Reconstruction with Predictive Filter Flow
Additional Tasks: