HyperRes: Hypernetwork-Based Adaptive Image Restoration (ICASSP 2023)

by Shai Aharon and Dr. Gil Ben-Artzi

Official Implementation: Hypernetwork-Based Adaptive Image Restoration

Project Page

Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model. We present an approach that is highly accurate and allows a significant reduction in the number of parameters. In contrast to existing methods, our approach can restore images using a single fixed-size model, regardless of the number of degradation levels. On popular datasets, our approach yields state-of-the-art results in terms of size and accuracy for a variety of image restoration tasks, including denoising, deJPEG, and super-resolution.

This allows the user to continuoesly select the level of restoration without retraining

For evaluation, we compared our method to 18 different networks, each has been separately trained on a single noise level from 5 to 90. Those networks determine the maximum accuracy that a network with similar architecture could achieve. Our method surpasses SOTA results, and achieves a near optimal accuracy on all the range, evan on extrapolation.

The graph below, compares our method and STOA methods to the optimal accuracy (the zero line).

Colab Demo

• This demo is intended to experience the "controllable" feature of our network, to experience the real-time adjustment I recommend looking at the Live Demo section below.

Requirements

Dependency

Our code is based on

• PyTorch 1.5.11+

Run the following command line to install all the dependencies

pip install -r requirements.txt

Dataset

We used the DIV2K dataset for training all tasks. For evaluation, we used a different dataset for each task, in compliance to the common benchmarks, as follows.

Task	Dataset	Comments
Training	DIV2K	Used to train all tasks
DeNoising	BDSD68	Trained and tested on RGB
DeJPEG	LIVE1	Trained and tested on Luminance (grayscale)
Super-Resolution	Set5	Trained on RGB and tested on Luminance (grayscale)

Links:

• DIV2K
• CBSD68
• LIVE1
• Set5

Pre-trained Models

We supply pre-trained models for each task

Task	Levels	Link
DeNoising	[15,35,55,75]	Link
Super Resolution	[2,3,4,5]	Link
DeJPEG	[10,30,50,80]	Link
Noise Detection	0-100	Link

Training

Dataset File structure

To train/test the dataset should be defined as follows:

[main folder]─┐
              ├─► train ──┐
              │           ├─► n_10 ──┐
              ├─► valid   │          ├─► *.png
              │           ├─► n_15   │
              └─► test    │          ├─► *.png
                          .          │
                          .          │
                          .          │
                          .          │
                          .          └─► *.png
                          │
                          ├─► n_90
                          │
                          └─► clean

• The structure for the valid/test folder is the same as train.
• 'n' is for DeNoising, 'j' is used for DeJPEG and 'sr' for Super Resolution
• [n/j/sr]_[Number] is the corruption folders, the letters represent the task, and the number is the corruption level.
• The images name in the 'clean' folder should mirror the names in the corruption folders

Train command:

python train_main.py       \
        --data [path to data]   \
        --data_type [n,sr,j]    \
        --lvls 15 35 50 75      \
        --checkpoint [SavePath] \
        --device [cpu/cuda]       

Testing

python test_main.py                          \
        --data [path to data]                     \ 
        --data_type [n,sr,j]                      \
        --lvls [15 45 75]                         \
        --valid [test folder inside data]         \                              
        --weights [path to weights file (*.pth)]  \ 
        --device [cpu/cuda]                       

Live Demo

python live_demo.py                                       \
        --input [Path to image folder or image]           \
        --data_type [n,sr,j]                              \
        --checkpoint [Path to weights file (*.pth)]       \ 

Citation

If you find either the code or the paper useful for your research, please cite our paper:

@misc{https://doi.org/10.48550/arxiv.2206.05970,
  doi = {10.48550/ARXIV.2206.05970},  
  url = {https://arxiv.org/abs/2206.05970},  
  author = {Aharon, Shai and Ben-Artzi, Gil},    
  title = {HyperRes: Hypernetwork-Based Adaptive Image Restoration},  
  publisher = {ICASSP},  
  year = {2023},  
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
}

Contact

For any questions and/or remarks feel free to contact me at shaiaha at bgu.ac.il