Depth of field

Model

Encoder Takes an input image and generates a high-dimensional feature vector Decoder Takes a high-dimensional feature vector and generates a semantic segmentation mask There are 3 major building blocks: Convolution Down-Sampling Up-Sampling

The encoder in the network computes progressively higher-level abstract features as the receptive fields in the encoder increase with the depth of the encoder. The spatial resolution of the feature maps is reduced progressively via a down-sampling operation, whereas the decoder computes feature maps of progressively increasing resolution via un-pooling (Zeiler and Fergus, 2014) or up-sampling

Examples

In this example, we will show how can we blur part of the background and emphasize the foreground. Starting with the original image:

We are calculating depth map and converting it into grayscale:

Based on the given threshold we extract the foreground and inverted mask

This helps us to make foreground image with transparent background:

When we concatenate this image with blured version of original image:

We get the end result and original below it:

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