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:

_images/street_640x360.png

Bicubic interpolation:

_images/x3c_street_640x360.png

Super resolution:

_images/x3_street_640x360.png

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.

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:

_images/street_640x360.png

Bicubic interpolation:

_images/x3c_street_640x360.png

Super resolution:

_images/x3_street_640x360.png

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.