Learning method, high resolution processing method, learning apparatus and computer program
An aspect of the present invention is a learning method for learning a model which reconstructs data and acquires reconstructed data, the learning method includes a plurality of stages, and the learning method includes first sub-processing which is based on an iterative calculation algorithm in which a Lagrange multiplier is not used, second sub-processing which is based on an iterative calculation algorithm in which the Lagrange multiplier is used, and weighting sub-processing which determines processing in each of the plurality of stages based on a weight for determining usage of the first sub-processing and the second sub-processing in each stage.
1 . A learning method for learning a model which reconstructs data, the learning method comprising a plurality of stages, the learning method comprising:
acquiring, by an observation device, image data, where the image data is obtained by imaging an imaging target with compressed sensing;
first sub-processing of the image data, where the first sub-processing is based on a half quadratic splitting method;
second sub-processing of the image data, where the second sub-processing is based on an alternating direction method of multipliers;
weighting sub-processing of the image data, where the weighting sub-processing determines processing in each of the plurality of stages based on a weight for determining usage of the first sub-processing and the second sub-processing in each stage;
executing each stage of the plurality of stages according to the weighting sub-processing; and
generating a learned model based on parameters obtained from executing each stage in the plurality of stages, where the learned model outputs a hyperspectral image.
2 . The learning method according to claim 1 , further comprises performing processing at each layer of a deep neural network using training data, thereby obtaining the parameters and generating the learned model.
3 . A non-transitory computer-readable medium having computer-executable instructions that, upon execution of the instructions by a processor of a computer, cause a computer to execute the learning method according to claim 1 .
4 . The learning method according to claim 1 wherein the learned model uses an observation matrix as input data.
5 . The learning method according to claim 1 wherein the observation device includes an optical system configured to disperse light.
6 . The learning method according to claim 1 further comprises initially training the learned model using the first sub-processing of the image data and subsequently training the learned model using the second sub-processing of the image data.
7 . A learning apparatus for learning a model which reconstructs data, the learning apparatus having a plurality of stages and comprising:
a processor; and
a storage medium having computer program instructions stored thereon, when executed by the processor, perform, in each of the plurality of stages, to:
acquire image data, where the image data is obtained by imaging an imaging target with compressed sensing;
execute a first sub-processing of the image data, where the first sub-processing is based on a half quadratic splitting method;
execute a second sub-processing of the image data, where the second sub-processing is based on an alternating direction method of multipliers;
execute a weighting sub-processing which determines processing in each of the plurality of stages based on a weight for determining usage of the first sub-processing and the second sub-processing in each stage;
executing each stage of the plurality of stages according to the weighting sub-processing; and
generating a learned model based on parameters obtained from executing each stage in the plurality of stages, where the learned model outputs a hyperspectral image.