![]() However, the default is largely driven by machine learning models that have been trained on millions of similar images in order to determine which settings to adjust and how people and objects "should" look. It’s possible to divorce the processing from the original image capture by invoking Raw shooting modes or turning to third-party apps. But in most cases, the result is remarkably close to what you saw with your eyes. Sometimes all this processing is evident, as in the case of people’s faces that look like skin smoothing has been applied or night scenes that look like late afternoon. Phone processors, like those from Apple and Google, contain powerful graphics and machine learning accelerator cores, like the 'Neural Engine' in the A14 chip. It balances the tone and color and writes the finished image to memory. It then blends them together, making adjustments based on what it identified in the scene, perhaps punching up the blue saturation and contrast in the sky, adding texture to a person’s hair or clothing, and prioritizing the in-focus captures to freeze moving subjects. As soon as you tap, the camera captures multiple images with different exposure and ISO settings within a few milliseconds. How? Using dedicated image processors and a pipeline stuffed full of machine learning.īefore you even tap the shutter button on the phone screen, the camera system evaluates the scene and makes choices based on what it detects, such as whether you’re making a portrait or capturing a landscape. The photos they produce should be small and unexceptional.Īnd yet, smartphone image quality is competing with photos made from cameras with larger sensors and better glass. It may seem as if the cameras are taking up ever more space on our phones, but remember that there are two, three, or more individual cameras working in tandem to give you approximately the same focal range as an inexpensive kit lens for a DSLR or mirrorless body. ![]() These cameras have tiny sensors and tiny lens elements. ![]() Photos from smartphone cameras shouldn’t look as good as they do. Today's smartphones use advanced imaging processing boosted by.you guessed it: machine learning! Some camera systems, such as the Canon EOS R3 and the Sony a7R V, can recognize specific people and focus on them when they’re in the frame. As machine learning algorithms have improved and processors have become faster at evaluating the images relayed from the sensor, newer cameras can now identify other specific items such as birds, animals, automobiles, planes, and even camera drones. Even when the focus target is positioned elsewhere in the frame, if the camera recognizes a face – and by extension, eyes – the focus point locks there. We’ve seen this for a while on cameras that feature face and eye detection for focusing on people. Based on machine learning models, the processor is also trying to interpret the scene in front of the camera and identify subjects (such as faces or objects) in the scene. Increasingly, there's a separate process going on in parallel. However, the autofocus system knows nothing about the scene. Depending on the AF mode, the processor is often evaluating one or more focus points or relying on your assistance to specify a focus area. ![]() It drives the lens until this contrast is maximized or the two perspectives come into alignment. Your camera generally surveys the scene in an uncomprehending manner, either hunting for contrast or analyzing different perspectives on the scene to assess how misaligned they are. Mirrorless cameras benefit greatly from machine learning and are trained to recognize everything from people to airplanes. Here are five areas where you’re probably already benefiting from machine learning, even if you’re not aware of it. As tools based on machine learning and AI have appeared, most recently Adobe’s Generative Fill feature in Photoshop, photographers seem to bounce between embracing the technology as a new creative tool and rejecting the intrusion of “AI” into a pursuit that values image authenticity and real-world experience.īut while generative AI has stolen all the attention lately, machine learning has long maintained a foothold in the photography field. ![]()
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