Computational photography

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Computational photography or computational imaging refers to computational image capture, processing, and manipulation techniques that enhance or extend the capabilities of digital photography. Typically, this involves modifying the design of a traditional camera by introducing novel optical elements to capture coded images which can be later decoded in post-processing to recover additional scene information. In recent years, an increase in the processing power of chips coupled with the introduction of cameras in cell phones has resulted in research in this field gaining much greater impetus. Good examples of image capture and manipulation include panoramas, and high-dynamic-range imaging which is the use of differently exposed pictures of the same scene to extend dynamic range beyond even that of analog film-based media.[1] 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.[2] 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. Epsilon Photography is a sub-field of computational photography.

Computational illumination[edit]

This is controlling photographic illumination in a structured fashion, then processing the captured images, to create new images. The applications include image-based relighting, image enhancement, image deblurring, geometry/material recovery and so forth.

Computational optics[edit]

This is capture of optically coded images, followed by computational decoding to produce new images. Coded aperture imaging was mainly applied in astronomy or X-ray imaging to boost the image quality. Instead of a single pin-hole, a pinhole pattern is applied in imaging, and deconvolution is performed to recover the image. In coded exposure imaging, the on/off state of the shutter is coded to modify the kernel of motion blur.[3] In this way motion deblurring becomes a well-conditioned problem. Similarly, in a lens based coded aperture, the aperture can be modified by inserting a broadband mask.[4] Thus, out of focus deblurring becomes a well-conditioned problem. The coded aperture can also improve the quality in light field acquisition using Hadamard transform optics.

Computational processing[edit]

This is processing of non-optically-coded images to produce new images.

Computational sensors[edit]

These are detectors that combine sensing and processing, typically in hardware, like the oversampled binary image sensor.

Early work in computer vision[edit]

Although computational photography is a currently popular buzzword in computer graphics, many of its techniques first appeared in the computer vision literature, either under other names or within papers aimed at 3D shape analysis.


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