Use of a saliency map to train a colorization ann

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 input image comprising a plurality of channels;
• determining a saliency map for the input image;
• establishing at least one of the plurality of channels as a training channel and at least some of the plurality of channels as one or more ground truth channels;
• training an artificial neural network (ANN) to predict one or more output channels from the training channel, wherein the training involves computationally updating weights of the ANN based on a loss function that comprises a difference between the one or more output channels and the one or more ground truth channels, and wherein the difference is computationally biased based on values from the saliency map;
• and
• providing the trained 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 input image comprising a plurality of channels;
• determining a saliency map for the input image;
• establishing at least one of the plurality of channels as a training channel and at least some of the plurality of channels as one or more ground truth channels;
• training an artificial neural network (ANN) to predict one or more output channels from the training channel, wherein the training involves computationally updating weights of the ANN based on a loss function that comprises a difference between the one or more output channels and the one or more ground truth channels, and wherein the difference is computationally biased based on values from the saliency map;
• and
• providing the trained ANN.