The process of capturing of the image as well as the processing of the neural engine is actually completed within a second , which is quite a remarkable step given the resolution of the images and the known computational lag the deep networks exhibit even in the inference mode.
The ability to generate inference on such high resolutions at such speeds is indeed a very remarkable step and a huge achievement by many yardsticks. The real magic however is not in the AI alone itself, the effective use of technology is also attributable to the cutting edge technology of the power boosted A13 Bionic chip  which makes all these supernatural speeds of calculations possible in the real world situation and gives definitive results in real-time. The domain of computation photography has been existing since a significant amount of time, but it was never possible to use it in real-time for practical implementations as the speeds were slow in the best case scenario. The introduction of new chip gives Apple the edge required to allow the use of the technology for real-time implementations.
Previous year Google, one of the big names in the field of computational photography, introduced Night Sight , which is a marvelous combination of camera hardware and intelligent image processing, which is capable of transformation of images taken in the darkness into having great detail for color and contents. Apple was expected to bring a similar product that was able to rival the features that are provided by the Google Night Stand and the response from apple is the introduction of Night Mode  on the new iPhone 11. The Night mode is capable of a number of mind-blowing features, whereby the images, taken in complete darkness that would in the previous versions will have absolutely no features visible can now have great amount of details captured in itself, as is evident in the comparison of images from the previous version of the iPhone itself to the latest light mode.
An image has a great many details captured in itself, the latest technology has enabled the automation of the tasks that otherwise would have required a large amount of time and dedication in the learning procedure. One such example of the same can be the auto-focus features that is nowadays so common in almost all the devices was up until a few years took a great deal of effort to learn that is now so effortlessly executed almost daily without a single day of training. The vision in the introduction of the discussed details actually attempts to repeat the success of the same, only instead of the hard coded logic we have artificial neurons which have learnt important characteristics on a wide variety of images and are now capable of making decisions on the important features as well as their intensities in a given image and can now extract important information from a given set of images (taken from the different cameras in the new iPhone with variable features) and integrate them into a single representation of the frame with all the information intact and the resultant image that can actually rival a professional.
Join our mailing list to receive the latest news and updates from our team. You'r information will not be shared.