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README.md

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+---
+language: en
+license: apache-2.0
+model_name: super-resolution-10.onnx
+tags:
+- validated
+- vision
+- super_resolution
+- sub_pixel_cnn_2016
+---
+<!--- SPDX-License-Identifier: Apache-2.0 -->
+
+# Super Resolution
+
+## Use cases
+The Super Resolution machine learning model sharpens and upscales the input image to refine the details and improve quality.
+
+## Description
+Super Resolution uses efficient  [Sub-pixel convolutional layer](https://arxiv.org/abs/1609.05158) described for increasing spatial resolution within network tasks. By increasing pixel count, images are then clarified, sharpened, and upscaled without losing the input image’s content and characteristics.
+
+## Model
+
+|Model      |Download  |Download (with sample test data)| ONNX version | Opset Version|
+|-------------|:--------------|:--------------|:--------------| :------------|
+|Super_Resolution|    [240 KB](model/super-resolution-10.onnx)  |  [7.6 MB](model/super-resolution-10.tar.gz) |  1.5.0  | 10|
+
+## Inference
+Get started with this model by running through the [included inference notebook](dependencies/Run_Super_Resolution_Model.ipynb) for Super Resolution or following the steps below.
+
+### Input
+Image input sizes are dynamic. The inference was done using jpg image.
+
+### Preprocessing
+Images are resized into (224x224). The image format is changed into YCbCr with color components: greyscale ‘Y’, blue-difference  ‘Cb’, and red-difference ‘Cr’. Once the greyscale Y component is extracted, it is then passed through the super resolution model and upscaled.
+
+from PIL import Image
+from resizeimage import resizeimage
+import numpy as np
+orig_img = Image.open('IMAGE_FILE_PATH')
+img = resizeimage.resize_cover(orig_img, [224,224], validate=False)
+img_ycbcr = img.convert('YCbCr')
+img_y_0, img_cb, img_cr = img_ycbcr.split()
+img_ndarray = np.asarray(img_y_0)
+img_4 = np.expand_dims(np.expand_dims(img_ndarray, axis=0), axis=0)
+img_5 = img_4.astype(np.float32) / 255.0
+img_5
+
+
+### Output
+The model outputs a multidimensional array of pixels that are upscaled. Output shape is [batch_size,1,672,672]. The second dimension is one because only the (Y) intensity channel was passed into the super resolution model and upscaled.
+
+### Postprocessing
+Postprocessing involves converting the array of pixels into an image that is scaled to a higher resolution. The color channels (Cb, Cr) are also scaled to a higher resolution using bicubic interpolation. Then the color channels are combined and converted back to RGB format, producing the final output image.
+
+final_img = Image.merge(
+"YCbCr", [
+img_out_y,
+img_cb.resize(img_out_y.size, Image.BICUBIC),
+img_cr.resize(img_out_y.size, Image.BICUBIC),
+]).convert("RGB")
+plt.imshow(final_img)
+
+
+## Dataset
+This model is trained on the [BSD300 Dataset](https://github.com/pytorch/examples/tree/master/super_resolution), using crops from the 200 training images.
+
+## Training
+View the  [training notebook](https://github.com/pytorch/examples/tree/master/super_resolution) to understand details for parameters and network for SuperResolution.
+