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Super Resolution Demo

This topic demonstrates how to run Super Resolution demo application, which reconstructs the high resolution image from the original low resolution one.

The corresponding pre-trained model is delivered with the product:

  • single-image-super-resolution-0034, which is the primary and only model that performs super resolution 4x upscale on a 200x200 image

For details on the model, please refer to the description in the deployment_tools/intel_models folder of the OpenVINO™ toolkit installation directory.

How It Works

On the start-up, the application reads command-line parameters and loads the specified network. After that, the application reads a 200x200 input image and performs 4x upscale using super resolution.

Running

Running the application with the -h option yields the following usage message:

./super_resolution_demo -h
InferenceEngine:
    API version ............ <version>
    Build .................. <number>

super_resolution_demo [OPTION]
Options:

    -h                      Print a usage message.
    -i "<path>"             Required. Path to an image.
    -m "<path>"             Required. Path to an .xml file with a trained model.
    -pp "<path>"            Path to a plugin folder.
    -d "<device>"           Specify the target device to infer on (CPU, GPU, FPGA, or MYRIAD). The demo will look for a suitable plugin for the specified device.
    -ni "<integer>"         Number of iterations (default value is 1)
    -pc                     Enable per-layer performance report

Running the application with the empty list of options yields the usage message given above and an error message.

To run the demo, you can use public models or a pre-trained and optimized model delivered with the package:

  • <INSTAL_DIR>/deployment_tools/intel_models/single-image-super-resolution-0034

To do inference on CPU using a trained model, run the following command:

./super_resolution_demo -i <path_to_image>/image.bmp -m <path_to_model>/model.xml

NOTE: Public models should be first converted to the Inference Engine format (*.xml + *.bin) using the Model Optimizer tool.

Demo Output

The application outputs a reconstructed high-resolution image and saves it in the current working directory as *.bmp file with sr prefix.

See Also