Estimation of metrics using a plenoptic imaging system

A non-transitory computer-readable medium storing a computer program product for estimating a metric from a plenoptic image of an object, the computer program product including instructions that, when executed by a processor, cause the processor to carry out the steps of:

• accessing a plenoptic image captured by a plenoptic imaging system, the plenoptic imaging system performing a plenoptic imaging process that is modeled by a pupil image function (PIF) that relates an object to a plenoptic image formed by that object;
• and
• estimating a metric that is a function of the object, based on modeling the plenoptic imaging process using the PIF;
• wherein:
• the step of estimating the metric includes applying a PIF inversion process to the captured plenoptic image;
• the object comprises K object components;
• the PIF comprises K component PIFs, each component PIF modeling the plenoptic imaging process for the corresponding object component;
• and the step of applying a PIF inversion process to the captured plenoptic image produces estimates of each object component;
• the plenoptic imaging process is modeled as a vector-matrix operation [Ivf]=[[PIFv1] [PIFv2] . . . [PIFvK]][[Ivo1]T[Ivo2]T . . . [IvoK]T]T, where the [Ivok] are vectors representing the object components, the [PIFvk] are matrices representing the component PIFs and [Ivf] is a vector representing the captured plenoptic image;
• and
• the step of applying a PIF inversion process comprises solving an optimization problem to minimize a metric with respect to the [Ivok]where the metric is a function of ∥[[PIFv1][PIFv2] . . . [PIFvK]][[Ivo1]T[Ivo2]T . . . [IvoK]T]T−[Ivf]∥ and ∥.∥ defines a norm or combination of norms.

A plenoptic imaging system comprising:

• a plenoptic imaging system including an overlapping first imaging subsystem and second imaging subsystem;
• the plenoptic imaging system performing a plenoptic imaging process that is modeled by a pupil image function (PIF) that relates an object to a plenoptic image formed by that object;
• the first imaging subsystem producing a primary image;
• the second imaging subsystem including:
• a secondary imaging array comprising an array of non-centrosymmetric lenslets positioned approximately at a location of the primary image, wherein non-centrosymmetric means a lenslet is not the same as the lenslet rotated by 180 degrees;
• and
• a sensor array positioned to capture a plenoptic image formed by the secondary imaging array;
• and
• a processor coupled to the sensor array, the processor accessing a plenoptic image captured by the plenoptic imaging system and estimating a metric that is a function of the object, based on modeling the plenoptic imaging process using the PIF;
• wherein:
• the step of estimating the metric includes applying a PIF inversion process to the captured plenoptic image;
• the object comprises K object components;
• the PIF comprises K component PIFs, each component PIF modeling the plenoptic imaging process for the corresponding object component;
• and the step of applying a PIF inversion process to the captured plenoptic image produces estimates of each object component;
• the plenoptic imaging process is modeled as a vector-matrix operation [Ivf]=[[PIFv1][PIFv2] . . . [PIFvK]][[Ivo1]T[Ivo2]T . . . [IvoK]T]T, where the [Ivok] are vectors representing the object components, the [PIFvk] are matrices representing the component PIFs and [Ivf] is a vector representing the captured plenoptic image;
• and
• the step of applying a PIF inversion process comprises solving an optimization problem to minimize a metric with respect to the [Ivok] where the metric is a function of ∥[[PIFv1][PIFv2] . . . [PIFvK]][[Ivo1]T[Ivo2]T . . . [IvoK]T]T−[Ivf]∥ and ∥.∥ defines a norm or combination of norms.