Models
The models module within our library provides implementations of deep learning models tailored for computational imaging \(\reconnet\). These models leverage the latest advancements in neural networks to offer robust solutions for image reconstruction, enhancement, and segmentation.
List of models
The models module contains the following models:
Autoencoder Model |
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Unet Model |
List of custom layers
The custom_layers module within our library provides implementations of custom layers that are used in the models. These layers are designed to enhance the performance of the models by providing additional flexibility and control over the network architecture.
Activation Layer |
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Convolutional Block |
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Spatial downsampling and then convBlock |
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Spatial upsampling and then convBlock |
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Spatial upsampling and then convBlock |
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Convolutional Block with 1x1 kernel and without activation |