Using machine learning techniques to obtain coherence functions

A non-transitory computer readable storage medium having program instructions embodied therewith, the program instructions executable by a computing device to cause the computing device to perform operations comprising:

• receiving an operational input sample;
• inputting the operational input sample into a trained neural network;
• obtaining, from the trained neural network, a spatial coherence function mapped to the operational input sample in response to the inputting the operational input sample into the trained neural network, wherein the spatial coherence function is usable to form an image according to Rsl=∫1M{circumflex over (R)}(m)dm, where Rsl represents an image pixel value, M represents a short-lag value, and {circumflex over (R)}(m) represents the spatial coherence function;
• and
• executing a computer-based instruction based on obtaining the spatial coherence function.

A system comprising:

• a processor, a computer readable memory, a non-transitory computer readable storage medium associated with a computing device, and program instructions executable by the computing device to cause the computing device to perform operations comprising:
• receiving an operational input sample;
• inputting the operational input sample into a trained neural network that maps a plurality of different sets of input samples to one or more spatial coherence functions;
• obtaining, from the trained neural network, a spatial coherence function mapped to the operational input sample in response to the inputting the operational input sample into the trained neural network, wherein the spatial coherence function is usable to form an image according to Rsl=∫1M{circumflex over (R)}(m)dm, where Rsl represents an image pixel value, M represents a short-lag value, and {circumflex over (R)}(m) represents the spatial coherence function;
• and
• executing a computer-based instruction based on obtaining the spatial coherence function.