Programmable Imaging: Towards a Flexible Camera

Nayar, Shree K.; Branzoi, Vlad; Boult, Terry

doi:10.1007/s11263-005-3102-6

Cited by 81 publications

(44 citation statements)

References 18 publications

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“…Gu et al [17] implemented coded rolling shutter for CMOS image sensors for high speed imaging, HDR imaging, and image deblurring. Nayar et al [18] implemented a pixel-wise coded exposure camera using a DMD (digital micro-mirror device) for HDR imaging, feature detection, and object recognition. Gupta et al [19] implemented a similar emulation system with a projector for motion-aware photography.…”

Section: Related Workmentioning

confidence: 99%

Video from a single coded exposure photograph using a learned over-complete dictionary

Hitomi

Gupta

et al. 2011

2011 International Conference on Computer Vision

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235

198

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Cameras face a fundamental tradeoff between the spatial and temporal resolution -digital still cameras can capture images with high spatial resolution, but most high-speed video cameras suffer from low spatial resolution. It is hard to overcome this tradeoff without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing and reconstructing the space-time volume in order to overcome this tradeoff. Our approach has two important distinctions compared to previous works: (1) we achieve sparse representation of videos by learning an over-complete dictionary on video patches, and (2) we adhere to practical constraints on sampling scheme which is imposed by architectures of present image sensor devices. Consequently, our sampling scheme can be implemented on image sensors by making a straightforward modification to the control unit. To demonstrate the power of our approach, we have implemented a prototype imaging system with per-pixel coded exposure control using a liquid crystal on silicon (LCoS) device. Using both simulations and experiments on a wide range of scenes, we show that our method can effectively reconstruct a video from a single image maintaining high spatial resolution.

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Section: Related Workmentioning

confidence: 99%

Video from a single coded exposure photograph using a learned over-complete dictionary

Hitomi

Gupta

et al. 2011

2011 International Conference on Computer Vision

Self Cite

235

198

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“…Hicks and Nayar et al proposed methods and system designs allowing to modify the shape of the mirror in a catadioptric system in order to adapt the sensor to a change in the scene or to acquire an image in a desired way [224,296,370].…”

Section: Programmable Systemsmentioning

confidence: 99%

“…Nayar et al proposed to use a programmable array of micromirrors to achieve programmable, or purposive, imaging [370,369]. They demonstrated the use of digital micro-mirror devices (DMDs), routinely used in projectors, for tasks such as high dynamic range image acquisition, optical appearance matching, or generally speaking, the change of imaging geometry.…”

Section: Programmable Systemsmentioning

confidence: 99%

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

Sturm

2010

FNT in Computer Graphics and Vision

157

122

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A printed and bound version is available at a special discount price of US35 from Now Publishers. This can be obtained by entering the promotional code CGV006023 on https://www.nowpublishers.com/bookorder.aspx?doi=0600000023&product=CGVInternational audienceThis survey is mainly motivated by the increased availability and use of panoramic image acquisition devices, in computer vision and various of its applications. Different technologies and different computational models thereof exist and algorithms and theoretical studies for geometric computer vision ("structure-from-motion") are often re-developed without highlighting common underlying principles. One of the goals of this survey is to give an overview of image acquisition methods used in computer vision and especially, of the vast number of camera models that have been proposed and investigated over the years, where we try to point out similarities between different models. Results on epipolar and multi-view geometry for different camera models are reviewed as well as various calibration and self-calibration approaches, with an emphasis on non-perspective cameras. We finally describe what we consider are fundamental building blocks for geometric computer vision or structure-from-motion: epipolar geometry, pose and motion estimation, 3D scene modeling, and bundle adjustment. The main goal here is to highlight the main principles of these, which are independent of specific camera models

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“…Most typical computational optics try to improve the imaging results by controlling the aperture and capturing optically coded images, i.e., coded aperture imaging. In previous work, various finely designed modulators have been inserted into the aperture or added in front of the camera for the capture of optically coded images, including masks [3][4][5][6][7], pinhole patterns [8], lenslets [9][10][11], filters or occluders [12][13][14][15], and mirrors [16][17][18][19][20]; these elements are used in specific applications, such as capture of the light field [21], long distance camera interaction [22], defocus deblurring [23], extending the depth of field or refocusing [4][5][6][7][23][24][25], extending the field of view [26,27], confocal imaging [20], enhancing the dynamic range [13,18,28,29], extracting the depth cues [15], and capturing multi-modal visual information [30].…”

Section: Computational Samplingmentioning

confidence: 99%

An overview of computational photography

Suo

Dai

2012

Sci. China Inf. Sci.

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Computational photography is an emerging multidisciplinary field. Over the last two decades, it has integrated studies across computer vision, computer graphics, signal processing, applied optics and related disciplines. Researchers are exploring new ways to break through the limitations of traditional digital imaging for the benefit of photographers, vision and graphics researchers, and image processing programmers. Thanks to much effort in various associated fields, the large variety of issues related to these new methods of photography are described and discussed extensively in this paper. To give the reader the full picture of the voluminous literature related to computational photography, this paper briefly reviews the wide range of topics in this new field, covering a number of different aspects, including: (i) the various elements of computational imaging systems and new sampling and reconstruction mechanisms; (ii) the different image properties which benefit from computational photography, e.g. depth of field, dynamic range; and (iii) the sampling subspaces of visual scenes in the real world. Based on this systematic review of the previous and ongoing work in this field, we also discuss some open issues and potential new directions in computational photography. This paper aims to help the reader get to know this new field, including its history, ultimate goals, hot topics, research methodologies, and future directions, and thus build a foundation for further research and related developments.

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Programmable Imaging: Towards a Flexible Camera

Cited by 81 publications

References 18 publications

Video from a single coded exposure photograph using a learned over-complete dictionary

Video from a single coded exposure photograph using a learned over-complete dictionary

Camera Models and Fundamental Concepts Used in Geometric Computer Vision

An overview of computational photography

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