Wolfram Language

Neural Networks

Model an Image as a Function

Train a network to reproduce an image by treating the image as a function from (x, y) pixel positions to (r, g, b) color values.

Convert an image into lists of rules associating normalized pixel positions with RGB values.

In[1]:=
Click for copyable input
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In[2]:=
Click for copyable input
dims = Reverse@ImageDimensions[img]; rules = Flatten@ MapIndexed[(2 (#2 - 1.)/(dims - 1) - 1.) -> #1 &, ImageData@img, {2}];
In[3]:=
Click for copyable input
Take[rules, 5]
Out[3]=
In[4]:=
Click for copyable input
Length[rules]
Out[4]=

Create a network to predict the color based on pixel position.

In[5]:=
Click for copyable input
chain = NetChain[{100, Ramp, 100, Ramp, 50, Tanh, 3}, "Input" -> 2]
Out[5]=

Train the network.

In[6]:=
Click for copyable input
trained = NetTrain[chain, rules, MaxTrainingRounds -> 200, BatchSize -> 2048];
Out[7]=

Use the network to predict the entire original image.

In[8]:=
Click for copyable input
{h, v} = Range[-1, 1, 2./#] & /@ dims; coords = Tuples[{h, v}]; Image[Partition[trained@coords, Length[v]]]
Out[8]=

Visualize the feature maps that are combined to produce the final image.

In[9]:=
Click for copyable input
featureNet = Take[trained, 4] {h, v} = Range[-1, 1, 60./#] & /@ dims; coords = Tuples[{h, v}]; features = Image[Partition[featureNet@coords, Length[v]], Magnification -> 2]; Multicolumn[ColorSeparate[ImageAdjust@features], Spacings -> {0.2, 0.2}]
Out[9]=

Related Examples

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