Computational photography (artistic)

Computational photography refers to digital image capture and processing techniques that use digital computation instead of optical processes. Computational photography can improve the capabilities of a camera, or introduce features that were not possible at all with film based photography, or reduce the cost or size of camera elements. Examples of computational photography include in-camera computation of digital panoramas, high-dynamic-range images, and light field cameras. Light field cameras use novel optical elements to capture three dimensional scene information which can then be used to produce 3D images, enhanced depth-of-field, and selective de-focusing (or 'post focus'). Enhanced depth-of-field reduces the need for mechanical focusing systems. All of these features use computational imaging techniques. Computational photography refers to digital image capture and processing techniques that use digital computation instead of optical processes. Computational photography can improve the capabilities of a camera, or introduce features that were not possible at all with film based photography, or reduce the cost or size of camera elements. Examples of computational photography include in-camera computation of digital panoramas, high-dynamic-range images, and light field cameras. Light field cameras use novel optical elements to capture three dimensional scene information which can then be used to produce 3D images, enhanced depth-of-field, and selective de-focusing (or 'post focus'). Enhanced depth-of-field reduces the need for mechanical focusing systems. All of these features use computational imaging techniques. The definition of computational photography has evolved to cover a number ofsubject areas in computer graphics, computer vision, and appliedoptics. These areas are given below, organized according to a taxonomyproposed by Shree K. Nayar. Within each area is a list of techniques, and foreach technique one or two representative papers or books are cited.Deliberately omitted from thetaxonomy are image processing (see also digital image processing)techniques applied to traditionally capturedimages in order to produce better images. Examples of such techniques areimage 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. 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. High-dynamic-range imaging uses differently exposed pictures of the same scene to extend dynamic range. Other examples include processing and merging differently illuminated images of the same subject matter ('lightspace'). 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. 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. 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. Coded aperture patterns can also be designed using color filters, in order to apply different codes at different wavelengths. This allows to increase the amount of light that reaches the camera sensor, compared to binary masks. Computational imaging is a set of imaging techniques that combine data acquisition and data processing to create the image of an object through indirect means to yield enhanced resolution, additional information such as optical phase or 3D reconstruction. The information is often recorded without using a conventional optical microscope configuration or with limited datasets. Computational imaging allows to go beyond physical limitations of optical systems, such as numerical aperture, or even obliterates the need for optical elements. For parts of the optical spectrum where imaging elements such as objectives are difficult to manufacture or image sensors cannot be miniaturized, computational imaging provides useful alternatives, in fields such as X-Ray and THz radiations.