SYSTEMS, METHODS, AND COMPUTER PROGRAM PRODUCTS FOR DIGITAL PHOTOGRAPHY USING A NEURAL NETWORK

1 . An apparatus, comprising:

a processor configured to:

receive a digital package comprising two or more images;

define a threshold associated with the two or more images;

perform a first action on the two or more images using a machine learning system;

based on a result of the first action, perform a second action using the machine learning system, wherein the machine learning system includes an adversarial structure comprising two or more neural network subsystems;

iteratively reperform the first action and the second action until the threshold is obtained; and

render a synthetic image based on the two or more images, the first action, the second action, and the threshold.

2 . The apparatus of claim 1 , wherein the apparatus is configured such that a first image of the two or more images is an ambient image and a second image of the two or more images is one of a flash image or a second ambient image.

3 . The apparatus of claim 1 , wherein the digital package further includes metadata, and a function or parameter.

4 . The apparatus of claim 3 , wherein the metadata includes data associated with the two or more images, including at least one of resolution, color, compression type, camera model number, camera processor type, lens make, a lens model, exposure information, lens configuration, slider positions, default settings, filters to be applied, user past behavior, user interaction with a camera, or at least one social networking connection.

5 . The apparatus of claim 1 , wherein the two or more neural network subsystems are part of a generative adversarial network (GAN).

6 . The apparatus of claim 1 , wherein the first action includes at least one of: rectifying chromatic aberrations associated with a lens, reducing an amount of noise, sharpening the two or more images, modifying a color contrast, or modifying a curve level.

7 . The apparatus of claim 1 , wherein the first action includes at least one of:

aligning a first image of the two or more images and a second image of the two or more images;

combining two images of the two or more images; or

implementing a setting found in metadata associated with the two or more images.

8 . The apparatus of claim 1 , wherein the second action includes at least one of: testing, training a database system, or validating the result of the first action.

9 . The apparatus of claim 1 , wherein the two or more neural network subsystems include an artificial intelligence system.

10 . The apparatus of claim 9 , wherein the artificial intelligence system is configured to at least one of: apply predictive modeling, train a database system, or learn based on the first action and the second action.

11 . The apparatus of claim 1 , wherein the apparatus is configured to output a second synthetic image based on the synthetic image and at least one image of the two or more images.

12 . The apparatus of claim 1 , wherein the synthetic image is stored as an object of the digital package and accessible over a network.

13 . The apparatus of claim 1 , wherein the synthetic image is rendered utilizing at least one server.

14 . The apparatus of claim 1 , wherein the synthetic image is rendered utilizing at least one client.

15 . The apparatus of claim 1 , wherein the synthetic image includes application code used to adjust viewing parameters for rendering the synthetic image.

16 . The apparatus of claim 15 , wherein the viewing parameters include at least one of a device type, a screen size, a processor type, an amount of RAM, or an input type.

17 . A computer program product comprising computer executable instructions stored on a non-transitory computer readable medium that when executed by a processor instruct the processor to:

receive a digital package comprising two or more images;

define a threshold associated with the two or more images;

perform a first action on the two or more images using a machine learning system;

based on a result of the first action, perform a second action using the machine learning system, wherein the machine learning system includes an adversarial structure comprising two or more neural network subsystems;

iteratively reperform the first action and the second action until the threshold is obtained; and

render a synthetic image based on the two or more images, the first action, the second action, and the threshold.

18 . A method, comprising:

receiving, using a processor of a server system, a digital package comprising two or more images;

defining, using the processor, a threshold associated with the two or more images;

performing a first action on the two or more images using a machine learning system;

based on a result of the first action, performing a second action using the machine learning system, wherein the machine learning system includes an adversarial structure comprising two or more neural network subsystems;

iteratively reperforming the first action and the second action until the threshold is obtained; and

rendering a synthetic image based on the two or more images, the first action, the second action, and the threshold.

19 . An apparatus, comprising:

a processor configured to:

receive, at a server system, a first image from a first device;

synthesize the first image, at the server system, using a first neural network;

test, at the server system, the synthesized first image using a second neural network;

repeat the synthesis and the test until a first threshold is satisfied; and

once the first threshold is satisfied, render a final image and send the final image to the first device.

20 . The apparatus of claim 19 , wherein the first neural network and the second neural network are part of a generative adversarial network, the synthesis of the first image includes at least one of rectifying chromatic aberrations associated with a lens, reducing an amount of noise, sharpening the two or more images, modifying a color contrast, or modifying a curve level, and the test of the synthesized first image includes at least one of training a database system or validating the synthesized image.