Systems and methods for multi-sensor image enhancement

A device, comprising:

• a memory;
• one or more image capture devices;
• a user interface;
• and
• one or more processors operatively coupled to the memory, wherein the one or more processors are configured to execute instructions causing the one or more processors to:
• obtain a first image from a first image capture device of the one or more image capture devices, wherein the first image has a first field of view (FOV) and a first set of quality characteristics;
• obtain a second image from a second image capture device of the one or more image capture devices, wherein the second image has a second FOV and a second set of quality characteristics, wherein the second FOV comprises an overlapping portion with the first FOV and a non-overlapping portion with the first FOV, and wherein the second set of quality characteristics is different than the first set of quality characteristics;
• obtain a first neural network, wherein the first neural network is configured to take the first and second images as input, and produce a modified second image having a modified second set of quality characteristics, wherein the modified second image and modified second set of quality characteristics comprise a result of the first neural network attempting to match the second set of quality characteristics to the first set of quality characteristics of the first image;
• and
• generate an output image based, at least in part, on the modified second image, wherein the output image has the second FOV and quality characteristics modified to attempt to match the first set of quality characteristics.

An image processing method, comprising:

• obtaining a first image from a first image capture device of an electronic device, wherein the first image has a first field of view (FOV) and a first set of quality characteristics;
• obtaining a second image from a second image capture device of the electronic device, wherein the second image has a second FOV and a second set of quality characteristics, wherein the second FOV comprises an overlapping portion with the first FOV and a non-overlapping portion with the first FOV, and wherein the second set of quality characteristics is different than the first set of quality characteristics;
• obtaining a first neural network, wherein the first neural network is configured to take the first and second images as input, and produce a modified second image having a modified second set of quality characteristics, wherein the modified second image and modified second set of quality characteristics comprise a result of the first neural network attempting to match the second set of quality characteristics to the first set of quality characteristics of the first image;
• and
• generating an output image based, at least in part, on the modified second image, wherein the output image has the second FOV and quality characteristics modified to attempt to match the first set of quality characteristics.