Deep learning-based image fusion for noise reduction and high dynamic range

Abstract

Electronic devices, methods, and program storage devices for leveraging machine learning to perform improved image fusion and/or noise reduction are disclosed. An incoming image stream may be obtained from an image capture device, wherein the incoming image stream comprises a variety of differently-exposed captures, e.g., EV0 images, EV− images, EV+ images, long exposure images, EV0/EV− image pairs, etc., which are received according to a particular pattern. When a capture request is received, two or more intermediate assets may be generated based on determined combinations of images from the incoming image stream, and the intermediate assets may then be fed into a neural network that has been trained to determine one or more sets of parameters to optimally fuse and/or noise reduce the intermediate assets. In some embodiments, the network may be trained to operate on levels of pyramidal decompositions of the intermediate assets independently, for increased efficiency and memory utilization.