MATLAB does not have a built-in "subpixelConv2dLayer" as a ready-to-use layer in the "Deep Learning Toolbox". The only workaround is to manually implement a Subpixel Convolution layer as a custom layer, often called a "PixelShuffle" operation. This is exactly what "Wenzhe Shi et al." proposed in their paper, they use sub-pixel convolution to efficiently upscale images by reshaping feature maps rather than using deconvolution or interpolation.
Refer to the following code to understand how to define a Custom Subpixel Layer:
classdef SubpixelLayer < nnet.layer.Layer
properties
Scale
end
methods
function layer = SubpixelLayer(scale, name)
layer.Name = name;
layer.Description = "Subpixel layer with scale " + scale;
layer.Scale = scale;
end
function Z = predict(layer, X)
% X is size HxWx(C*r^2)xN
r = layer.Scale;
[H, W, C_mul_r2, N] = size(X);
C = C_mul_r2 / (r^2);
if mod(C_mul_r2, r^2) ~= 0
error('Number of channels must be divisible by scale^2');
end
X = reshape(X, H, W, r, r, C, N);
X = permute(X, [1 3 2 4 5 6]); % H, r, W, r, C, N
X = reshape(X, H*r, W*r, C, N);
Z = X;
end
end
end
This "SubpixelLayer" can be integrated as a new layer in the model, provided that the decoder includes a convolutional layer producing "C × r²" output channels:
% r is the upscale factor
convLayer = convolution2dLayer(3, numChannels * r^2, 'Padding', 'same');
subpixelLayer = SubpixelLayer(r, 'subpixel');
% Add to layerGraph
lgraph = addLayers(lgraph, [
convLayer
subpixelLayer
]);
Kindly refer to the following MATLAB documentation to understand more about defining custom Deep Learning Layers with Learnable Parameters: https://www.mathworks.com/help/deeplearning/ug/define-custom-deep-learning-layer.html
