edge-SR: Super Resolution For The Masses

Citation

Pablo Navarrete Michelini, Yunhua Lu and Xingqun Jiang. "edge-SR: Super-Resolution For The Masses", in IEEE Winter conference on Applications of Computer Vision (WACV), 2022.

BibTeX

@inproceedings{eSR,
    title     = {edge--{SR}: Super--Resolution For The Masses},
    author    = {Navarrete~Michelini, Pablo and Lu, Yunhua and Jiang, Xingqun},
    booktitle = {Proceedings of the {IEEE/CVF} Winter Conference on Applications of Computer Vision ({WACV})},
    month     = {January},
    year      = {2022},
    pages     = {1078--1087},
    url       = {https://arxiv.org/abs/2108.10335}
}

Instructions:

Place input images in input directory (provided as empty directory). Color images will be converted to grayscale.
To upscale images run: python run.py.

Output images will come out in output directory.
The GPU number and model file can be changed in run.py (in comment "CHANGE HERE").

Requirements:

Python 3, PyTorch, NumPy, Pillow, OpenCV

Experiment results

The data directory contains the file tests.pkl that has the Python dictionary with all our test results on different devices. The following sample code shows how to read the file:

>>> import pickle
>>> test = pickle.load(open('tests.pkl', 'rb'))
>>> test['Bicubic_s2']
    {'psnr_Set5': 33.72849620514912,
     'ssim_Set5': 0.9283912810369976,
     'lpips_Set5': 0.14221979230642318,
     'psnr_Set14': 30.286027790636204,
     'ssim_Set14': 0.8694934108301432,
     'lpips_Set14': 0.19383049915943826,
     'psnr_BSDS100': 29.571233006609656,
     'ssim_BSDS100': 0.8418117904964167,
     'lpips_BSDS100': 0.26246454380452633,
     'psnr_Urban100': 26.89378248655882,
     'ssim_Urban100': 0.8407461069831571,
     'lpips_Urban100': 0.21186692919582129,
     'psnr_Manga109': 30.850672809780587,
     'ssim_Manga109': 0.9340133711400112,
     'lpips_Manga109': 0.102985977955641,
     'parameters': 104,
     'speed_AGX': 18.72132628065749,
     'power_AGX': 1550,
     'speed_MaxQ': 632.5429857814075,
     'power_MaxQ': 50,
     'temperature_MaxQ': 76,
     'memory_MaxQ': 2961,
     'speed_RPI': 11.361346064182795,
     'usage_RPI': 372.8714285714285}

The keys of the dictionary identify the name of each model and its hyper--parameters using the following format:

Bicubic_s#,
eSR-MAX_s#_K#_C#,
eSR-TM_s#_K#_C#,
eSR-TR_s#_K#_C#,
eSR-CNN_s#_C#_D#_S#,
ESPCN_s#_D#_S#, or
FSRCNN_s#_D#_S#_M#,

where # represents an integer number with the value of the correspondent hyper-parameter. For each model the data of the dictionary contains a second dictionary with the information displayed above. This includes: number of model parameters; image quality metrics PSNR, SSIM and LPIPS measured in 5 different datasets; as well as power, speed, CPU usage, temperature and memory usage for devices AGX (Jetson AGX Xavier), MaxQ (GTX 1080 MaxQ) and RPI (Raspberry Pi 400).

License

pnavarre/eSR

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