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single-image-super-resolution-1033

Use Case and High-Level Description

An Attention-Based Approach for Single Image Super Resolution but with reduced number of channels and changes in network architecture. It enhances the resolution of the input image by a factor of 3.

Example

Low resolution:

Bicubic interpolation:

Super resolution:

Specification

Metric	Value
PSNR	30.97 dB
GFlops	16.062
MParams	0.030
Source framework	PyTorch*

For reference, PSNR for bicubic upsampling on test dataset is 28.48 dB.

Inputs

1. Image, name: 0, shape: 1, 3, 360, 640 in the format B, C, H, W, where:

   • B - batch size

   • C - number of channels

   • H - image height

   • W - image width

2. Bicubic interpolation of the input image, name: 1, shape: 1, 3, 1080, 1920 in the format B, C, H, W, where:

   • B - batch size

   • C - number of channels

   • H - image height

   • W - image width

   Expected color order is BGR.

Outputs

The net output is a blob with shapes 1, 3, 1080, 1920 that contains image after super resolution.

Legal Information

[*] Other names and brands may be claimed as the property of others.

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