Use Case and High-Level Description

Single image super resolution network based on SRResNet architecture ("Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network") but with reduced number of channels and depthwise convolution in decoder. It enhances the resolution of the input image by a factor of 4.

Example

Low resolution:

Linear interpolation:

Super resolution:

Specification

For reference, PSNR for bicubic upsampling on test dataset is 26.35 dB.

Performance

Link to performance table

Inputs

1. name: "input" , shape: [1x3x200x200] - An input image in the format [BxCxHxW], where:

   • B - batch size
   • C - number of channels
   • H - image height
   • W - image width.

   Expected color order is BGR.

Outputs

1. The net outputs one blobs with shapes [1, 3, 800, 800] that contains image after super resolution.

Legal Information

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