Abstract: An image processing pipeline includes an image processing system having multiple neural networks arranged to receive multiple input images, with the images having identifiable objects and noise features. A first neural network provides image information to a second neural network that recurrently processes the image information to both improve output presentation of identifiable objects and reduce noise features. In some embodiments other local or remote neural networks can be arranged to modify at least one of an image capture setting, sensor processing, global post processing, local post processing, portfolio post processing, or provide latent vectors or neural embedding information.

Abstract: An image processing pipeline including a still or video camera includes an image processing system having a first neural network arranged to receive at least one input image from a video camera having noise features and feedback from a neural embedding. The neural network processes at least one input image and feedback from the neural embedding to provide a temporally consistent output image having reduced noise as compared to noise features in the at least one input image. In some embodiments a second neural network in the image processing system is arranged to modify at least one of an image capture setting, sensor processing, global post processing, local post processing, portfolio post processing, or provide latent vectors or neural embedding information.