Convolutional neural network filter stacker for colorization preprocessing

A computing device, comprising:

• one or more processors;
• and
• non-transitory data storage storing at least computer-readable instructions that, when executed by the one or more processors, cause the computing device to perform operations comprising:
• receiving an image pair, wherein a first image of the image pair comprises a respective initial training image and wherein a second image of the image pair comprises a respective ground truth training image;
• providing instances of the first image to a plurality of image filtering modules, wherein each image filtering module is configured to apply an image filter on an input image and output a filtered representation of the input image;
• determining respective filtered representations of the first image using the plurality of image filtering modules;
• indirectly training an adaptor artificial neural network (ANN) by at least:
• applying the adaptor ANN on at least the respective filtered representations of the first image to produce an adapted representation of the first image;
• determining, using a trained colorization ANN, a colorized image from the adapted representation;
• and
• computationally updating weights of the adaptor ANN based on a loss function that comprises a difference between the colorized image and the second image of the image pair;
• and
• providing the plurality of image filtering modules, the trained adaptor ANN, and the trained colorization ANN.

An article of manufacture comprising non-transitory data storage storing at least computer-readable instructions that, when executed by one or more processors of a computing device, cause the computing device to perform operations comprising:

• receiving an image pair, wherein a first image of the image pair comprises a respective initial training image and wherein a second image of the image pair comprises a respective ground truth training image;
• providing instances of the first image to a plurality of image filtering modules, wherein each image filtering module is configured to apply an image filter on an input image and output a filtered representation of the input image;
• determining respective filtered representations of the first image using the plurality of image filtering modules;
• indirectly training an adaptor artificial neural network (ANN) by at least:
• applying the adaptor ANN on at least the respective filtered representations of the first image to produce an adapted representation of the first image;
• determining, using a trained colorization ANN, a colorized image from the adapted representation;
• and
• computationally updating weights of the adaptor ANN based on a loss function that comprises a difference between the colorized image and the second image of the image pair;
• and
• providing the plurality of image filtering modules, the trained adaptor ANN, and the trained colorization ANN.