Computational photography combines plentiful computing, digital sensors, modern optics, actuators, probes and smart lights to escape the limitations of traditional film cameras and enables novel imaging applications. Unbounded dynamic range, variable focus, resolution, and depth of field, hints about shape, reflectance, and lighting, and new interactive forms of photos that are partly snapshots and partly videos are just some of the new applications found in Computational Photography. The computational techniques encompass methods from modification of imaging parameters during capture to sophisticated reconstructions from indirect measurements. We provide a practical guide to topics in image capture and manipulation methods for generating compelling pictures for computer graphics and for extracting scene properties for computer vision, with several examples.

Many ideas in computational photography are still relatively new to digital artists and programmers and there is no up-to-date reference text. A larger problem is that a multi-disciplinary field that combines ideas from computational methods and modern digital photography involves a steep learning curve. For example, photographers are not always familiar with advanced algorithms now emerging to capture high dynamic range images, but image processing researchers face difficulty in understanding the capture and noise issues in digital cameras. These topics, however, can be easily learned without extensive background. The goal of this STAR is to present both aspects in a compact form.