It’s essentially about improving the automatic settings for better point-and-shoot photos.
LUMINAR AI REDDIT SOFTWARE
What is computational photography?Ĭomputational photography goes beyond hardware – the lens and sensors of a camera – and uses machine vision software to enhance what photos are captured. After a while you might start to wonder who is actually in charge. It's a calculating, analysing, 'thinking' device that doesn't just capture the scene as it is, but how it thinks you want it to be, or how it thinks you ought to want it to be.ĪI can be like having a know-all assistant. A camera phone is no longer 'just' a camera. Multi-camera arrays and computational imaging have long since merged to produce a hybrid technology that replicates many of the depth of field and lens effects you get from larger cameras. “Top-end smartphones are increasingly featuring dual-lens cameras, but the Google Pixel 5 uses a single camera lens with computational photography to replicate an optical zoom and add various effects.” “Software is becoming more important for smartphones because they have a physical lack of optics, so we’ve seen the rise of computational photography that tries to replicate an optical zoom,” says imaging analyst Arun Gill, Senior Market Analyst at Futuresource Consulting. “What is more surprising is that deep neural networks work so well that they started to replace algorithms that, until very recently, were never associated with AI, such as demosaicking and de-noising.” Voice-activated camerasĪI is mostly about new kinds of software, initially to make up for smartphones’ lack of zoom lenses. That auto-mode is now getting smarter is hardly surprising. “It was pretty obvious that ‘classical’ AI algorithms such as auto exposure, auto white balance and auto focus would get a huge boost thanks to machine learning and deep neural networks,” says Jérôme Abribat, Communication and Content Marketing Director at DxO. Is that 'intelligence', or simply a slightly more advanced implementation of exposure measurement, subject movement and focus distance? Multi-pattern metering systems typically use a complex measurement of light distribution based on thousands of real-world photos and have been using a 'deep learning' process before the term had been invented.Ĭameras have always used algorithms – that’s what ‘auto-mode’ is – but now comes ‘real’ AI. For years, compact camera makers have been offering different subject orientated scene modes which can be chosen automatically by the camera. It can be difficult to separate true AI from sophisticated automation. AI cameras can automatically blend HDR images in bright light, switch to a multi-image capture mode in low light and use the magic of computational imaging to create a stepless zoom effect with two or more camera modules. “Smartphones equipped with powerful AI processors are able to quickly and automatically recognise what is in a scene, as well as automatically differentiate between foreground subjects and backgrounds,” says Wang. Not only that, but it could soon take charge of editing and curating our existing photography libraries tooĪI cameras think and learn about settings and image processing. It may not be time to chuck out the DSLR quite yet, but AI seems set to change how we take photos. Right now it largely applies to smartphone cameras, but the incredible algorithms and sheer level of automated software that the technology is allowing will soon prove irresistible to most of us. AI is quickly becoming an overused term in the world of photography. 'AI', 'deep learning', 'machine learning', computer vision’ and 'neural networks' are all intertwined in this new branch of technology.īut what’s AI got to do with cameras? Computational photography and time-saving photo editing, that’s wha, and even voice-activation. There is a whole cluster of buzzwords around this topic. It's generally split into subsets of technology that try to emulate what humans do, such as speech recognition, voice-to-text dictation, image recognition, pattern recognition and face scanning. AI is a genre of computer science that examines if we can teach a computer to think or, at least, learn.
0 kommentar(er)
