Digital photography captures images on electronic sensors instead of film. Learn how the medium evolved from a 0.01-megapixel prototype in 1975 to computational photography and AI.
In 1975, an engineer at Kodak named Steven Sasson assembled a device the size of a toaster from scavenged parts: a Super 8 movie camera lens, a digital tape recorder, and a charge-coupled device sensor made by Fairchild Semiconductor. The device captured a black-and-white image on a 100-by-100 pixel grid, recorded it onto a cassette tape, and displayed it on a television screen. The exposure took 23 seconds. The image was 0.01 megapixels. Sasson's supervisor told him the technology was interesting but not to tell anyone about it, because it would be decades before it was viable. Kodak, which invented the digital camera, spent the next 35 years trying to suppress the technology that would destroy its film business. They succeeded in delaying the transition and failed in surviving it.
Digital photography is the practice of capturing images using electronic sensors that convert light into digital data, rather than using the chemical emulsions of film photography. The sensor, typically a CMOS (complementary metal-oxide-semiconductor) or CCD (charge-coupled device) array, contains millions of photosites that measure the intensity of light striking them. This data is processed by the camera's image processor into a digital file, usually in RAW or JPEG format, that can be stored, edited, printed, or transmitted electronically.
This entry covers how digital photography works, its history from Sasson's prototype to contemporary computational photography, the artists who have used digital tools, and how AI image generation is challenging the definition of photography itself.
The core of a digital camera is the image sensor. A sensor is a silicon chip containing a grid of millions of photosites, commonly called pixels, though technically a pixel is the output unit and a photosite is the physical sensor element. When light strikes a photosite, it generates an electrical charge proportional to the light intensity. This charge is converted to a digital value by an analog-to-digital converter. The resulting data is a grid of numbers, each representing the brightness at one photosite.
Because silicon photosites are inherently sensitive to all visible light, color information requires a color filter array placed over the sensor. The most common is the Bayer filter, patented by Bryce Bayer at Eastman Kodak in 1976. The Bayer filter arranges red, green, and blue filters over the photosites in a pattern with twice as many green filters as red or blue, matching the human eye's greater sensitivity to green light. The camera's image processor uses a demosaicing algorithm to interpolate full color information for each pixel from the filtered data.
A 2026 review published in Reports on Progress in Physics examined how cameras have been transformed from devices that record focal images into analog-to-digital converters that transform massively parallel optical signals into serial electronic data. The review describes a shift from cameras optimized to produce two-dimensional images toward cameras engineered as end-to-end information channels, co-designing optics, focal-plane, and readout to deliver more task-relevant measurements per photon and per joule. This is the technical foundation of computational photography, where the camera does not simply capture an image but constructs one from multiple captures, sensor data, and algorithmic processing. Read the review at IOPscience.
The digital camera was invented at Kodak in 1975, but the first commercially available digital camera was the Dycam Model 1, released in 1990, which captured 320x240 pixel black-and-white images. The Apple QuickTake 100, released in 1994, was one of the first consumer digital cameras, capturing 640x480 pixel images. These early cameras produced images that were unusable for anything beyond web display, and their quality was far below film.
The turning point came in the early 2000s. The Canon EOS D30, released in 2000, was the first digital SLR with an affordable CMOS sensor, producing 3.1-megapixel images. The Canon EOS-1Ds, released in 2002, offered 11.1 megapixels and a full-frame sensor, matching 35mm film in resolution for the first time. Professional photographers began switching to digital. By 2007, when the iPhone was introduced with a 2-megapixel camera, the transition was underway at every level of the market.
The smartphone revolution transformed digital photography more fundamentally than any camera innovation. In 2010, Apple introduced HDR (High Dynamic Range) capture on the iPhone 4, which merged multiple exposures to produce a single image with greater tonal range. This was the beginning of computational photography: the idea that the camera's software, not just its optics and sensor, determines the final image. Google's Pixel phones, introduced in 2016, pushed computational photography further with features like HDR+ and Night Sight, which used machine learning to produce usable images in near-darkness. By 2026, smartphone cameras use neural processing units to perform real-time scene recognition, semantic segmentation, and tone mapping that would have required a desktop computer and manual editing a decade earlier.
Gursky's large-scale digital photographs, including Rhein II (1999, which sold for $4.3 million in 2011, the highest price ever paid for a photograph at auction) use digital manipulation to create images of extraordinary scale and precision. Rhein II depicts the Rhine river as a flat, horizontal band of water between green banks under an overcast sky. Gursky digitally removed a factory building from the scene, producing an image that is photographically derived but not a straight photograph. The work demonstrates how digital tools allow photographers to construct images that exceed what a camera can capture in a single exposure.
Crewdson's Beneath the Roses series (2003-2008) uses digital compositing to create cinematic photographs of small-town American scenes. Each image is assembled from multiple exposures, with lighting, actors, and environments staged and combined digitally. The results look like single photographs but are constructed images that could not exist as straight captures. Crewdson's practice demonstrates how digital photography can move beyond documentation into fabrication.
Ruff has explored the technical foundations of digital photography throughout his career. His jpeg series (2004-present) takes low-resolution JPEG images from the internet and enlarges them to enormous sizes, making the compression artifacts, the pixel grid and block structure of the JPEG format, visible as the subject of the work. The series is both a critique of digital image quality and a meditation on the material structure of digital photographs, which, unlike film grain, are based on mathematical compression algorithms.
Sherman, known for her self-portrait series in which she transforms herself into different characters using makeup, costumes, and staging, transitioned to digital photography in the 2000s. Her Society Portraits (2008) used digital manipulation to exaggerate the artifice of her subjects' appearances, creating images that comment on digital beauty standards and the constructed nature of identity in the age of social media.
Burtynsky's large-format photographs of industrial landscapes, including the Anthropocene project (2018), use digital capture and stitching to produce images of extraordinary detail and scale. His photographs of oil fields, quarries, and manufacturing sites are made with high-resolution digital sensors and composited from multiple captures, producing prints that reveal environmental detail at scales from the microscopic to the continental.
The most significant development in digital photography since the introduction of the CMOS sensor is the convergence of photography with artificial intelligence. This convergence operates on two levels. First, AI is embedded in the capture process itself. Smartphone cameras use neural networks for scene detection, face tracking, semantic segmentation (identifying which parts of the image are sky, skin, foliage, or building), and tone mapping. The image that comes out of a 2026 smartphone camera is not a direct record of the light that struck the sensor. It is a computational construction based on sensor data, processed by algorithms trained on millions of images.
Second, generative AI models can produce images that look like photographs but were not captured by a camera. A 2026 article in the Journal of Aesthetics and Art Criticism examined the ontological status of photographs in the era of generative AI, arguing that AI-generated images should be distinguished from photographs and that maintaining this distinction is necessary for productive aesthetic discourse. The article advances the view that "just as photography was not the death of painting, so AI is unlikely to be the death of photography." Read the article at Oxford Academic.
A 2026 article in the International Journal for Digital Art History traced the converging histories of photography and AI art, arguing that both are data-driven image-making technologies and that photography's historical reception provides a framework for situating AI within critical art history. The article notes that the controversy surrounding AI image-making echoes the 19th-century debates about whether photography was art or mere mechanical reproduction. Read the article at Heidelberg University Publishing.
Digital photography in 2026 is the dominant form of image-making worldwide. Smartphone cameras have largely replaced dedicated cameras for casual photography, and the number of digital photographs taken each year is estimated in the trillions. Professional photographers use high-resolution digital cameras, medium format digital backs, and mirrorless systems that offer image quality exceeding 35mm film in resolution and dynamic range.
The question of what counts as a photograph is increasingly contested. If a smartphone camera uses AI to identify a face, adjust skin tones, brighten shadows, and sharpen edges, is the result a photograph or a computational image derived from photographic data? If a generative AI model produces an image that looks like a photograph, is it a photograph? These questions are not merely academic. They affect copyright law, journalism ethics, and the definition of digital art as a practice.
Meanwhile, the film photography revival continues alongside digital, offering a material, chemical alternative to the computational image. The two mediums coexist, serving different purposes and different photographers. Digital photography offers speed, flexibility, and infinite reproducibility. Film offers physicality, constraint, and a tangible negative. The choice between them is now an aesthetic and philosophical decision, not a practical necessity.
Digital photographs are best seen as large-format prints, where the resolution and tonal range of contemporary sensors can be appreciated. Andreas Gursky's monumental prints are held in the collection of the Museum of Modern Art in New York and the Tate Modern in London. Edward Burtynsky's work is exhibited internationally at institutions including the National Gallery of Canada and the Metropolitan Museum of Art. Thomas Ruff's jpeg series is held in collections at the Stedelijk Museum in Amsterdam and the Pinakothek der Moderne in Munich.
For more on photographic processes, read our entries on film photography, alternative process photography, and digital art, or explore our blog post on how to read a painting to learn more about analyzing visual art across mediums.
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