:fire: PNG Upscale - AI Super Resolution :fire:

Small tool using pretrained models to upscale images

Download is available from the Releases Page or Google Drive or MediaFire

• Hosted Folder include full "Models" folder 📁 and executable Files 🖼️ to download

• Windows 64bit [Exe] / [Jar]

• Linux 64bit [Jar]

• macOS 64bit [Jar]

• :electron: Download the executable corresponding with your operating system, and the Models folder

• It's possible to download only some of the models if you want (It just wont let you use them inside the program)

• The Models folder needs to be in the same directory as the Jar/Exe to use them

• ⚠️ Be careful when trying to resize very large pictures, it can take considerable time and resources ⚠️

• To upscale an image you just need to choose a mode, load a picture and press start

• Save button can be used to choose an output folder and filename before you start the process (either just name or .png)

• You can double click the text box to change [Dark <-> Light] theme (disabled when upScaling)

• Use PNG images for best results

• if faced with a JNI Error see this issue for a possible fix https://github.com/Araxeus/PNG-Upscale/issues/33

Models

all of the model download links below are already included in the MediaFire folder.

There are four trained models integrated into the program :

EDSR

[Best Quality]+[Slowest]

• Size of the model: ~38.5MB x3. This is a quantized version, so that it can be uploaded to GitHub. (Original was 150MB each.)
• This model was trained for 3 days with a batch size of 16
• Link to implementation code: https://github.com/Saafke/EDSR_Tensorflow
• x2, x3, x4 trained models available
• Advantage: Highly accurate
• Disadvantage: Slow and large filesize
• Speed: < 3 sec for every scaling factor on 256x256 images on an Intel i7-9700K CPU.
• Original paper: Enhanced Deep Residual Networks for Single Image Super-Resolution [1]

Trained models can be downloaded from here.

ESPCN

[Fast]

• Size of the model: ~100kb x3
• This model was trained for ~100 iterations with a batch size of 32
• Link to implementation code: https://github.com/fannymonori/TF-ESPCN
• x2, x3, x4 trained models available
• Advantage: It is tiny and fast, and still performs well.
• Disadvantage: Perform worse visually than newer, more robust models.
• Speed: < 0.01 sec for every scaling factor on 256x256 images on an Intel i7-9700K CPU.
• Original paper: Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network [2]

Trained models can be downloaded from here.

FSRCNN

[Fast]

• Size of the model: ~40KB x3
• This model was trained for ~30 iterations with a batch size of 1
• Link to implementation code: https://github.com/Saafke/FSRCNN_Tensorflow
• Advantage: Fast, small and accurate
• Disadvantage: Not state-of-the-art accuracy
• Speed: < 0.01 sec for every scaling factor on 256x256 images on an Intel i7-9700K CPU.
• Notes: FSRCNN-small has fewer parameters, thus less accurate but faster.
• Original paper: Accelerating the Super-Resolution Convolutional Neural Network [3]

Trained models can be downloaded from here.

LapSRN

[Has x8]

• Size of the model: between 1-5Mb x3
• This model was trained for ~50 iterations with a batch size of 32
• Link to implementation code: https://github.com/fannymonori/TF-LAPSRN
• x2, x4, x8 trained models available
• Advantage: The model can do multi-scale super-resolution with one forward pass. It can now support 2x, 4x, 8x, and [2x, 4x] and [2x, 4x, 8x] super-resolution.
• Disadvantage: It is slower than ESPCN and FSRCNN, and the accuracy is worse than EDSR.
• Speed: < 0.1 sec for every scaling factor on 256x256 images on an Intel i7-9700K CPU.
• Original paper: Deep laplacian pyramid networks for fast and accurate super-resolution [4]

Trained models can be downloaded from here.

Benchmarks

Comparing different algorithms. Scale x4 on monarch.png

As a Demo this image was resized from 256x256 to 85x85, and then upscaled using this program

x2 Demo (85x85 -> 170x170)

Original	Bicubic Interpolation	EDSR

ESPCN	FSRCNN	LapSRN

Bicubic Interpolation is the standart resizing technique used by most editing tools like photoship etc..

x4 Demo (85x85 -> 340x340)

Original	Bicubic Interpolation	EDSR

ESPCN	FSRCNN	LapSRN

References

[1] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Kyoung Mu Lee, "Enhanced Deep Residual Networks for Single Image Super-Resolution", 2nd NTIRE: New Trends in Image Restoration and Enhancement workshop and challenge on image super-resolution in conjunction with CVPR 2017. [PDF] [arXiv] [Slide]

[2] Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A., Bishop, R., Rueckert, D. and Wang, Z., "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network", Proceedings of the IEEE conference on computer vision and pattern recognition CVPR 2016. [PDF] [arXiv]

[3] Chao Dong, Chen Change Loy, Xiaoou Tang. "Accelerating the Super-Resolution Convolutional Neural Network", in Proceedings of European Conference on Computer Vision ECCV 2016. [PDF] [arXiv] [Project Page]

[4] Lai, W. S., Huang, J. B., Ahuja, N., and Yang, M. H., "Deep laplacian pyramid networks for fast and accurate super-resolution", In Proceedings of the IEEE conference on computer vision and pattern recognition CVPR 2017. [PDF] [arXiv] [Project Page]