There is a paradigm shift taking place in the imaging industry with the introduction of computational photography.
This field involves digital image capture and processing techniques that use digital computation or algorithms instead of standard optical processes. It is very unconventional and may seem to be at odds with traditional photography. However, I see it as complementing traditional photography. Computational photography introduces new features in imaging that bring visualization beyond what traditional conventional photography offers, and it takes it to another level. The application for it is in 3D imaging, AR (Augmented Reality), VR (Virtual Reality) and MR (Mixed Reality) environments which are bringing new ways of storytelling for filmmakers and allowing creatives to explore new ways of shooting video, developing video games and creating digital content. We are already seeing its applications on consumer devices, most noticeably on smartphones.
Computational photography was first used as a phrase by Canadian inventor and engineer Steve Mann back in 1995 to describe his work in computer imaging. One of the pioneers who gets credit for the field of computational photography by giving it a broader meaning was from an electrical engineering and computer science professor at Stanford University named Marc Levoy. Levoy is also a distinguished engineer at Google, where he works in development of imaging systems like Google’s HDR+. According to Levoy, computational photography is “computational imaging techniques that enhance or extend the capabilities of digital photography [in which the] output is an ordinary photograph, but one that could not have been taken by a traditional camera.”
The camera obscura method has been the traditional way of capturing images. By focusing an optical device to a subject with good light, an image is created on medium like film. The creation of the image ends in the dark room after it has been developed on film. Then cameras became digital and captured images to storage devices. It contains more information about the image like metadata that can be used in post processing. The image creation ends after the digital image has been retouched or processed from it’s RAW file. In the digital age of photography, cameras are not just capturing an image but also data. The image creation process actually has so many possibilities because of that, there is no end to a final “perfect” image. Computational photography makes the best use of that data that has been captured from exposures to create the best images. In combination with AI techniques it can produce even better results that is open to many possibilities.
Traditional photographers may not be on board for something that is far from traditional. A typical photographer creates images with a camera thru their lenses using artistic composition that is usually done correctly the first time it is shot on camera. Computational photography is basically the same, but it makes use of computing technology that may utilize AI machine learning techniques, advanced HDR and enhanced image processing to make the image look more stunning and visually appealing. It makes the photographer irrelevant in most cases since the process is intelligent enough to make the image look great even if it wasn’t shot by a professional. Using a Pixel or iPhone smartphone camera, an ordinary user can create stunning portraits with the help of computational photography.
One technique in computational photography I am quite familiar with and used in the past is HDR (High Dynamic Range) photography. This requires taking multiple exposures of the same scene and compositing them together to bring the details to life using software. There is also a technique of how to shoot HDR, while some DSLR cameras have HDR ready features available. This whole process can take about 20 minutes to an hour to perform by hand. However, with newer systems like in smartphones, it is all done in-camera without the user having to open an image editing software. The smartphone software is intelligent enough to know what to do and how to create the best HDR image from the exposures.