# single-image-super-resolution-1032¶

## 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 4.

## Example¶

Low resolution:

Bicubic interpolation:

Super resolution:

## Specification¶

Metric

Value

PSNR

29.29 dB

GFlops

11.654

MParams

0.030

Source framework

PyTorch*

For reference, PSNR for bicubic upsampling on test dataset is 26.79 dB.

## Inputs¶

1. Image, name: 0, shape: 1, 3, 270, 480 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.