Computational Photography

Computational photography refers to computational image capture, processing, and manipulation techniques that enhance or extend the capabilities of digital photography, typically through the use of multiple pictures of the same subject matter, such, as, for example, by using differently exposed pictures of the same scene to extend dynamic range beyond even that of analog film-based media. Other examples of computational photography include processing and merging differently illuminated images of the same subject matter ("lightspace") and differently focused pictures of the same subject matter. The output of these techniques is an ordinary photograph, but one that could not have been taken by a traditional camera.

Its current definition has evolved to cover a number of subject areas in computer graphics, computer vision, and applied optics. These areas are given below, organized according to a taxonomy proposed by Shree K. Nayar. Within each area is a list of techniques, and for each technique one or two representative papers or books are cited. Deliberately omitted from the taxonomy are image processing (see also digital image processing) techniques applied to traditionally captured images in order to produce better images. Examples of such techniques are image scaling, dynamic range compression (i.e. tone mapping), color management, image completion (a.k.a. inpainting or hole filling), image compression, digital watermarking, and artistic image effects. Also omitted are techniques that produce range data, volume data, 3D models, 4D light fields, 4D, 6D, or 8D BRDFs, or other high-dimensional image-based representations.