Generalized mosaicing: polarization panorama

Schechner, Yoav Y.; Nayar, Shree K.

doi:10.1109/tpami.2005.79

Cited by 28 publications

(14 citation statements)

References 53 publications

(69 reference statements)

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“…However, specular dielectric objects, such as water bodies (lakes) and shiny construction materials (e.g., windows) reflect light towards the camera that can be significantly polarized. 9,15,16,20 If such objects are close, then the electric field associated with their polarization may dominate over the partially polarized airlight.…”

Section: Handling Specular Objectsmentioning

confidence: 99%

Advanced visibility improvement based on polarization filtered images

Namer

Schechner

2005

SPIE Proceedings

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Recent studies have shown that major visibility degradation effects caused by haze can be corrected for by analyzing polarization-filtered images. The analysis is based on the fact that the path-radiance in the atmosphere (airlight) is often partially polarized. Thus, associating polarization with path-radiance enables its removal, as well as compensation for atmospheric attenuation. However, prior implementations of this method suffered from several problems. First, they were based on mechanical polarizers, which are slow and rely on moving part. Second, the method had failed in image areas corresponding to specular objects, such as water bodies (lakes) and shiny construction materials (e.g., windows). The reason for this stems from the fact that specular objects reflect partially polarized light, confusing a naive association of polarization solely with path-radiance. Finally, prior implementations derived necessary polarization parameters by manually selecting reference points in the field of view. This human intervention is a drawback, since we would rather automate the process. In this paper, we report our most recent progress in the development of our visibility-improvement method. We show directions by which those problems can be overcome. Specifically, we added algorithmic steps which automatically extract the polarization parameters needed, and make visibility recovery more robust to polarization effects originating from specular objects. In addition, we now test an electrically-switchable polarizer based on a liquid crystal device for improving acquisition speed.

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Section: Handling Specular Objectsmentioning

confidence: 99%

Advanced visibility improvement based on polarization filtered images

Namer

Schechner

2005

SPIE Proceedings

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“…Hence, fast temporal multiplexing by filter wheels or beam splitting [Pezzaniti et al 2008] is applied. Alternatively, the generalized mosaic of Schechner and Nayar [Schechner and Nayar 2005] can be used, which has also been applied to other plenoptic dimensions.…”

Section: Polarization Imagingmentioning

confidence: 99%

A reconfigurable camera add-on for high dynamic range, multispectral, polarization, and light-field imaging

Manakov

Tamayo

Klehm³

et al. 2013

ACM Trans. Graph.

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We propose a non-permanent add-on that enables plenoptic imaging with standard cameras. Our design is based on a physical copying mechanism that multiplies a sensor image into a number of identical copies that still carry the plenoptic information of interest. Via different optical filters, we can then recover the desired information. A minor modification of the design also allows for aperture subsampling and, hence, light-field imaging. As the filters in our design are exchangeable, a reconfiguration for different imaging purposes is possible. We show in a prototype setup that high dynamic range, multispectral, polarization, and light-field imaging can be achieved with our design.

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“…The computational sensor attempts to design or modify the detectors in some way to obtain task-specific imaging results. Similar to the computational optics, various plug-in elements have been designed, including lens arrays [2,32], masks [6,33,34], filter arrays [35][36][37], and mirrors [38], for task specific imaging results, e.g. capturing the light field [2,6,32], removing veiling glare [34,39], extending the dynamic range [35][36][37], and increasing the field of view [38,40].…”

Section: Computational Samplingmentioning

confidence: 99%

“…Similar to the computational optics, various plug-in elements have been designed, including lens arrays [2,32], masks [6,33,34], filter arrays [35][36][37], and mirrors [38], for task specific imaging results, e.g. capturing the light field [2,6,32], removing veiling glare [34,39], extending the dynamic range [35][36][37], and increasing the field of view [38,40]. Some other implementations of computational sensors include the introduction of sensor motion to extend the depth of field [41,42] or perform motion deblurring [43], and building sensing patterns for image super resolution [44] or high dynamic range imaging [45,46] 3) Computational illumination.…”

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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Generalized mosaicing: polarization panorama

Cited by 28 publications

References 53 publications

Advanced visibility improvement based on polarization filtered images

Advanced visibility improvement based on polarization filtered images

A reconfigurable camera add-on for high dynamic range, multispectral, polarization, and light-field imaging

An overview of computational photography

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