Computational photography offers numerous new capabilities. This example mixes HDR (High Dynamic Range) imaging and panoramics (image stitching) by optimally merging information from numerous variably exposed images of overlapping subject matter.[1][2][3][4][5]
Computational photography refers to digital picture capture and processing approaches that rely on digital computing rather than optical processes. Computational photography can enhance a camera’s capabilities, provide features that were previously impossible with film-based photography, and lower the cost or size of camera elements. Computational photography includes in-camera computing of digital panoramas,[6] high-dynamic-range photos, and light field cameras. Light field cameras use innovative optical elements to gather three-dimensional scene information, which is subsequently used to produce 3D images, increased depth-of-field, and selective de-focusing (or “post focus”). Increased depth-of-field eliminates the need for mechanical focusing mechanisms. Each of these features employs computational imaging techniques.
The notion of computational photography has expanded to include computer graphics, computer vision, and applied optics. These areas are listed below, categorized using a taxonomy established by Shree K. Nayar[citation needed]. Each topic contains a list of techniques, each with one or two exemplary papers or books listed. Image processing (also known as digital image processing) techniques used to improve traditionally obtained photographs are purposefully left out of the taxonomy. Such approaches include picture scaling, dynamic range compression (i.e. tone mapping), color management, image completion (also known as inpainting or hole filling), and image
Compression, digital watermarking, and creative visual effects. Techniques for creating range data, volume data, 3D models, 4D light fields, 4D, 6D, or 8D BRDFs, and other high-dimensional image-based representations are also excluded. Epsilon photography is a subfield in computational photography.
Effect on Photography
Photos taken with computational photography can allow amateurs to make photos of comparable quality to professional photographers, but as of 2019, they do not surpass the usage of professional-level equipment.[7]
Computational illumination
This involves manipulating photographic illumination in an organized manner and then processing the collected photos to produce new images. Applications include image-based relighting, image improvement, image deblurring, geometry/material recovery, and so on.
High-dynamic-range imaging extends dynamic range by using variably exposed images of the same scene.[8] Additional examples include processing and Combining various lighted images of the same subject (“lightspace”).
Computational optics
This is the capture of optically coded images, followed by algorithmic decoding to generate new images. Coded aperture imaging was primarily used in astronomy and X-ray imaging to improve image quality. Instead of a single pinhole, imaging uses a pinhole pattern, and the image is recovered via deconvolution.[9] In coded exposure imaging, the shutter’s on/off state is coded to affect the kernel of motion blur.[10] In this sense, motion deblurring becomes a well-defined task. Similarly, in a lens-based coded aperture, the aperture can be adjusted by introducing a broadband mask.[11] Thus, out-of-focus deblurring becomes a well-conditioned issue. A coded aperture might also enhance the quality.
Light field collection with Hadamard transform optics.
Color filters can also be used to create coded aperture patterns so that different codes can be applied at various wavelengths.[12][13] This permits more light to reach the camera sensor than binary masks.
Computational Imaging
Computational imaging is a set of imaging techniques that combine data acquisition and processing to produce a picture of an object indirectly, resulting in improved resolution, additional information such as optical phase, or 3D reconstruction. The data is frequently recorded without utilizing a traditional optical microscope arrangement or with little datasets.
Computational imaging allows us to overcome physical constraints of optical systems, such as numerical aperture [14], and even eliminates the requirement for optical elements.[15]
For Parts In sectors such as X-ray[16] and THz radiations, computational imaging presents valuable alternatives to imaging elements such as objectives or image sensors that are difficult to construct.
Common Techniques
Lensless imaging, computational speckle imaging, [17] ptychography, and Fourier ptychography are some of the most common computational imaging techniques.
Computational imaging approaches frequently use compressive sensing or phase retrieval techniques to rebuild an object’s angular spectrum. Other techniques in computational imaging include digital holography, computer vision, and inverse issues like tomography.
computational processing
This is the process of converting non-optically coded images into new ones.
Computational sensors
These are detectors that integrate sensing and processing, usually in hardware, such as the oversampled binary image sensor. Early work on computer vision
Although computational photography is now a famous buzzword in computer graphics, many of its approaches originated in the computer vision literature, either under different titles or in works aiming at 3D shape analysis.
A 1981 wearable computational photography equipment.
Art history.
Wearable Computational Photography began in the 1970s and early 1980s and has now grown into a more contemporary art form. This image is on the cover of the John Wiley and Sons textbook on the subject.
Computational photography is an art form that involves capturing variably exposed images of the same subject matter and combining them. This inspired the creation of wearable computers in the 1970s and early 1980s. Computational Photography was inspired by Charles Wyckoff’s work, hence computational photography datasets (e.g., variously exposed photos of the same subject collected to create a single composite image) are frequently referred to as Wyckoff Sets in his honor.
Mann and Candoccia did early work in this field (estimating image projection and exposure value together).
Charles Wyckoff spent most of his life developing special types of 3-layer photographic films that captured different exposures of the same scene. A photograph of a nuclear explosion made with Wyckoff’s film featured on the cover of Life Magazine, demonstrating the dynamic range from dark outer parts to inner core.
