Computational photography

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Computational photography provides many new capabilities. This example combines HDR (High Dynamic Range) Imaging with panoramics (image-stitching), by optimally combining information from multiple differently exposed pictures of overlapping subject matter Process nocomparam.png
Computational photography provides many new capabilities. This example combines HDR (High Dynamic Range) Imaging with panoramics (image-stitching), by optimally combining information from multiple differently exposed pictures of overlapping subject matter

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, [6] 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.

Contents

The definition of computational photography 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 [ citation needed ]. 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.

Effect on photography

Photos taken using computational photography can sometimes look better than those taken by professionals using significantly more expensive equipment. [7]

Computational illumination

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. [8] Other examples include processing and merging differently illuminated images of the same subject matter ("lightspace").

Computational optics

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. [9] In coded exposure imaging, the on/off state of the shutter is coded to modify the kernel of motion blur. [10] 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. [11] 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. [12] [13] This allows to increase the amount of light that reaches the camera sensor, compared to binary masks.

Computational imaging

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 [14] , or even obliterates the need for optical elements [15] .

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 [16] and THz radiations.

Common techniques

Among common computational imaging techniques are lensless imaging, computational speckle imaging [17] , ptychography and Fourier ptychography.

Computational imaging technique often draws on compressive sensing or phase retrieval techniques, where the angular spectrum of the object is being reconstructed. Other techniques are related to the field of computational imaging, such as digital holography, computer vision and inverse problems such as tomography.

Computational processing

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

Computational sensors

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

Early work in computer vision

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.

Art history

A 1981 wearable computational photography apparatus. Lightspace-wearcomp-lightcomb.jpg
A 1981 wearable computational photography apparatus.
Wearable Computational Photography originated in the 1970s and early 1980s, and has evolved into a more recent art form. This picture was used on the cover of the John Wiley and Sons textbook on the subject. Computational-photography-lightvectoring-as-art-form.jpg
Wearable Computational Photography originated in the 1970s and early 1980s, and has evolved into a more recent art form. This picture was used on the cover of the John Wiley and Sons textbook on the subject.

Computational photography, as an art form, has been practiced by capture of differently exposed pictures of the same subject matter, and combining them together. This was the inspiration for the development of the wearable computer in the 1970s and early 1980s. Computational photography was inspired by the work of Charles Wyckoff, and thus computational photography datasets (e.g. differently exposed pictures of the same subject matter that are taken in order to make a single composite image) are sometimes referred to as Wyckoff Sets, in his honor.

Early work in this area (joint estimation of image projection and exposure value) was undertaken by Mann and Candoccia.

Charles Wyckoff devoted much of his life to creating special kinds of 3-layer photographic films that captured different exposures of the same subject matter. A picture of a nuclear explosion, taken on Wyckoff's film, appeared on the cover of Life Magazine and showed the dynamic range from dark outer areas to inner core.

See also

Related Research Articles

Pinhole camera simple camera

A pinhole camera is a simple camera without a lens but with a tiny aperture, a pinhole camera – effectively a light-proof box with a small hole in one side. Light from a scene passes through the aperture and projects an inverted image on the opposite side of the box, which is known as the camera obscura effect.

High-dynamic-range imaging high dynamic range technique used in imaging and photography

High-dynamic-range imaging (HDRI) is a high dynamic range (HDR) technique used in imaging and photography to reproduce a greater dynamic range of luminosity than what is possible with standard digital imaging or photographic techniques. The aim is to present a similar range of luminance to that experienced through the human visual system. The human eye, through adaptation of the iris and other methods, adjusts constantly to adapt to a broad range of luminance present in the environment. The brain continuously interprets this information so that a viewer can see in a wide range of light conditions.

The light field is a vector function that describes the amount of light flowing in every direction through every point in space. The space of all possible light rays is given by the five-dimensional plenoptic function, and the magnitude of each ray is given by the radiance. Michael Faraday was the first to propose that light should be interpreted as a field, much like the magnetic fields on which he had been working for several years. The phrase light field was coined by Andrey Gershun in a classic paper on the radiometric properties of light in three-dimensional space (1936).

Vignetting reduction of an images brightness or saturation toward the periphery compared to the image center

In photography and optics, vignetting (; French: vignette) is a reduction of an image's brightness or saturation toward the periphery compared to the image center. The word vignette, from the same root as vine, originally referred to a decorative border in a book. Later, the word came to be used for a photographic portrait that is clear at the center and fades off toward the edges. A similar effect is visible in photographs of projected images or videos off a projection screen, resulting in a so-called "hotspot" effect.

Light-field camera

A light field camera, also known as plenoptic camera, captures information about the light field emanating from a scene; that is, the intensity of light in a scene, and also the direction that the light rays are traveling in space. This contrasts with a conventional camera, which records only light intensity.

Digital photography Photography with a digital camera

Digital photography uses cameras containing arrays of electronic photodetectors to capture images focused by a lens, as opposed to an exposure on photographic film. The captured images are digitized and stored as a computer file ready for further digital processing, viewing, electronic publishing, or digital printing.

The following are common definitions related to the machine vision field.

In optics and signal processing, wavefront coding refers to the use of a phase modulating element in conjunction with deconvolution to extend the depth of field of a digital imaging system such as a video camera.

The following outline is provided as an overview of and topical guide to photography:

The merits of digital versus film photography were considered by photographers and filmmakers in the early 21st century after consumer digital cameras became widely available. Digital photography and digital cinematography have both advantages and disadvantages relative to still film and motion picture film photography. In the 21st century, photography came to be predominantly digital, but traditional photochemical methods continue to serve many users and applications.

Coded aperture

Coded apertures or coded-aperture masks are grids, gratings, or other patterns of materials opaque to various wavelengths of electromagnetic radiation. The wavelengths are usually high-energy radiation such as X-rays and gamma rays. By blocking radiation in a known pattern, a coded "shadow" is cast upon a plane. The properties of the original radiation sources can then be mathematically reconstructed from this shadow. Coded apertures are used in X- and gamma ray imaging systems, because these high-energy rays cannot be focused with lenses or mirrors that work for visible light.

Image quality can refer to the level of accuracy in which different imaging systems capture, process, store, compress, transmit and display the signals that form an image. Another definition refers to image quality as "the weighted combination of all of the visually significant attributes of an image". The difference between the two definitions is that one focuses on the characteristics of signal processing in different imaging systems and the latter on the perceptual assessments that make an image pleasant for human viewers.

Range imaging is the name for a collection of techniques that are used to produce a 2D image showing the distance to points in a scene from a specific point, normally associated with some type of sensor device.

Light writing

Light writing is an emerging form of stop motion animation wherein still images captured using the technique known as light painting or light drawing are put in sequence thereby creating the optical illusion of movement for the viewer.

Femto-photography is a technique for recording the propagation of ultrashort pulses of light through a scene at a very high speed. A femto-photograph is equivalent to an optical impulse response of a scene and has also been denoted by terms such as a light-in-flight recording or transient image. Femto-photography of macroscopic objects was first demonstrated using a holographic process in the 1970s by Nils Abramsson at the Royal Institute of Technology (Sweden). A research team at the MIT Media Lab led by Ramesh Raskar, together with contributors from the Graphics and Imaging Lab at the Universidad de Zaragoza, Spain, more recently achieved a significant increase in image quality using a streak camera synchronized to a pulsed laser and modified to obtain 2D images instead of just a single scanline.

Imaging particle analysis is a technique for making particle measurements using digital imaging, one of the techniques defined by the broader term particle size analysis. The measurements that can be made include particle size, particle shape (morphology or shape analysis and grayscale or color, as well as distributions of statistical population measurements.

Gradient domain image processing, also called Poisson image editing, is a type of digital image processing that operates on the differences between neighboring pixels, rather than on the pixel values directly. Mathematically, an image gradient represents the derivative of an image, so the goal of gradient domain processing is to construct a new image by integrating the gradient, which requires solving Poisson's equation.

Epsilon photography is a form of computational photography wherein multiple images are captured with slightly varying camera parameters such as aperture, exposure, focus, film speed and viewpoint for the purpose of enhanced post-capture flexibility. The term was coined by Prof. Ramesh Raskar. The technique has been developed as an alternative to light field photography that requires no specialized equipment. Examples of epsilon photography include focal stack photography, High dynamic range (HDR) photography, lucky imaging, multi-image panorama stitching and confocal stereo. The common thread for all the aforementioned imaging techniques is that multiple images are captured in order to produce a composite image of higher quality, such as richer color information, wider-field of view, more accurate depth map, less noise/blur and greater resolution.

Fourier ptychography

Fourier ptychography is a computational imaging technique based on optical microscopy that consists in the synthesis of a wider numerical aperture from a set of full-field images acquired at various coherent illumination angles, resulting in increased resolution compared to a conventional microscope.

Computational Imaging is the process of indirectly forming images from measurements using algorithms that rely on a significant amount of computing. In contrast to traditional imaging, computational imaging systems involve a tight integration of the sensing system and the computation in order to form the images of interest. The ubiquitous availability of fast computing platforms, the advances in algorithms and modern sensing hardware is resulting in imaging systems with significantly enhanced capabilities. Computational Imaging systems cover a broad range of applications include computational microscopy, tomographic imaging, MRI, ultrasound imaging, computational photography, Synthetic Aperture Radar (SAR), seismic imaging etc. The integration of the sensing and the computation in computational imaging systems allows for accessing information which was otherwise not possible. For example:

References

  1. Steve Mann. "Compositing Multiple Pictures of the Same Scene", Proceedings of the 46th Annual Imaging Science & Technology Conference, May 9–14, Cambridge, Massachusetts, 1993
  2. S. Mann, C. Manders, and J. Fung, "The Lightspace Change Constraint Equation (LCCE) with practical application to estimation of the projectivity+gain transformation between multiple pictures of the same subject matter" IEEE International Conference on Acoustics, Speech, and Signal Processing, 6–10 April 2003, pp III - 481-4 vol.3.
  3. joint parameter estimation in both domain and range of functions in same orbit of the projective-Wyckoff group" ", IEEE International Conference on Image Processing,Vol.3, 16-19,pp.193-196 September 1996
  4. Frank M. Candocia: Jointly registering images in domain and range by piecewise linear comparametric analysis. IEEE Transactions on Image Processing 12(4): 409-419 (2003)
  5. Frank M. Candocia: Simultaneous homographic and comparametric alignment of multiple exposure-adjusted pictures of the same scene. IEEE Transactions on Image Processing 12(12): 1485-1494 (2003)
  6. Steve Mann and R. W. Picard. "Virtual bellows: constructing high-quality stills from video.", In Proceedings of the IEEE First International Conference on Image ProcessingAustin, Texas, November 13–16, 1994
  7. https://cacm.acm.org/magazines/2019/7/237705-the-edge-of-computational-photography/fulltext.Missing or empty |title= (help)
  8. ON BEING `UNDIGITAL' WITH DIGITAL CAMERAS: EXTENDING DYNAMIC RANGE BY COMBINING DIFFERENTLY EXPOSED PICTURES, IS&T's (Society for Imaging Science and Technology's) 48th annual conference, Cambridge, Massachusetts, May 1995, pages 422-428
  9. Martinello, Manuel. "Coded Aperture Imaging" (PDF).
  10. Raskar, Ramesh; Agrawal, Amit; Tumblin, Jack (2006). "Coded Exposure Photography: Motion Deblurring using Fluttered Shutter" . Retrieved November 29, 2010.
  11. Veeraraghavan, Ashok; Raskar, Ramesh; Agrawal, Amit; Mohan, Ankit; Tumblin, Jack (2007). "Dappled Photography: Mask Enhanced Cameras for Heterodyned Light Fields and Coded Aperture Refocusing" . Retrieved November 29, 2010.
  12. Martinello, Manuel; Wajs, Andrew; Quan, Shuxue; Lee, Hank; Lim, Chien; Woo, Taekun; Lee, Wonho; Kim, Sang-Sik; Lee, David (2015). "Dual Aperture Photography: Image and Depth from a Mobile Camera" (PDF). International Conference on Computational Photography.
  13. Chakrabarti, A.; Zickler, T. (2012). "Depth and deblurring from a spectrally-varying depth-of-field". IEEE European Conference on Computer Vision. 7576: 648–666.
  14. Ou et al., "High numerical aperture Fourier ptychography: principle, implementation and characterization" Optics Express 23, 3 (2015)
  15. Boominathan et al., "Lensless Imaging: A Computational Renaissance" (2016)
  16. Miyakawa et al., "Coded aperture detector : an image sensor with sub 20-nm pixel resolution", Optics Express 22, 16 (2014)
  17. Katz et al., "Non-invasive single-shot imaging through scattering layers and around corners via speckle correlations", Nature Photonics 8, 784–790 (2014)