Machine-learning techniques for sparse-to-dense spectral reconstruction

One or more non-transitory computer readable media including instructions that, when executed by one or more processors, cause the one or more processors to reconstruct representations of items in a spectral domain by performing the steps of:

• mapping a first set of data points associated with a both a first item and the spectral domain to conditioning information via a first trained machine learning model;
• updating a second trained machine learning model based on the conditioning information to generate a model that represents the first item within the spectral domain;
• generating a second set of data points associated with both the first item and the spectral domain via the model;
• and
• constructing an image associated with the first item based on the second set of data points.

A system comprising:

• one or more memories storing instructions;
• and
• one or more processors coupled to the one or more memories that, when executing the instructions, perform the steps of:
• mapping a first set of data points associated with a both a first item and a spectral domain to conditioning information via a first trained machine learning model;
• updating a second trained machine learning model based on the conditioning information to generate a model that represents the first item within the spectral domain;
• generating a second set of data points associated with both the first item and the spectral domain via the model;
• and
• constructing an image associated with the first item based on the second set of data points.