Computational photography

uses software algorithms to enhance and manipulate digital images, often in real-time as the photo is being taken. This technology allows smartphone cameras to overcome the physical limitations of their small sensors and lenses, producing images that would have been impossible just a few years ago.

One of the most visible applications of computational photography is in portrait mode, which uses AI to separate the subject from the background and apply a pleasing bokeh effect. This simulates the shallow depth of field typically associated with larger camera sensors and fast lenses.

Night mode is another area where computational photography shines. By capturing multiple exposures and intelligently combining them, smartphones can now produce clear, well-lit images in near-darkness, a feat that was once the exclusive domain of high-end cameras with large sensors.

AI-powered scene recognition is becoming increasingly sophisticated, allowing smartphones to automatically adjust camera settings for optimal results based on what’s being photographed. Whether it’s a landscape, a plate of food, or a fast-moving subject, the camera can adapt in real-time to capture the best possible image.

Looking forward, we can expect computational photography to continue pushing the boundaries of smartphone imaging. Developments in AI and machine learning will likely lead to even more impressive low-light performance, improved dynamic range, and the ability to capture motion in ways that were previously impossible with small sensors.