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.

Legal Information

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