Improving resolution and depth-of-field of light field cameras using a hybrid imaging system

Boominathan, Vivek; Mitra, Kaushik; Veeraraghavan, Ashok

doi:10.1109/iccphot.2014.6831814

Cited by 105 publications

(108 citation statements)

References 29 publications

Supporting

Mentioning

108

Contrasting

Order By: Relevance

“…In our 4 Please note that while images of size 1080×1080 pixels are natively output by a (first generation) Lytro camera, studies [40] have pointed out that the sensor can in reality only output an effective spatial resolution of 380×380. To yield sharp enough ground truth data in focused regions, we have therefore downscaled the images by a factor 3, namely to 360×360 pixels.…”

Section: Dataset Generationmentioning

confidence: 99%

Non-Parametric Blur Map Regression for Depth of Field Extension

D'Andres

Salvador

Kochale

et al. 2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

Abstract-Real camera systems have a limited depth of field (DOF) which may cause an image to be degraded due to visible misfocus or too shallow DOF. In this paper, we present a blind deblurring pipeline able to restore such images by slightly extending their DOF and recovering sharpness in regions slightly out-of-focus. To address this severely ill-posed problem, our algorithm relies first on the estimation of the spatiallyvarying defocus blur. Drawing on local frequency image features, a machine learning approach based on the recently introduced Regression Tree Fields is used to train a model able to regress a coherent defocus blur map of the image, labeling each pixel by the scale of a defocus point-spread-function. A non-blind spatiallyvarying deblurring algorithm is then used to properly extend the DOF of the image. The good performance of our algorithm is assessed both quantitatively, using realistic ground truth data obtained with a novel approach based on a plenoptic camera, and qualitatively with real images.Index Terms-Out-of-focus deblurring, extension of depth of field, Regression tree fields, defocus blur map.

show abstract

Section: Dataset Generationmentioning

confidence: 99%

Non-Parametric Blur Map Regression for Depth of Field Extension

D'Andres

Salvador

Kochale

et al. 2016

IEEE Trans. on Image Process.

View full text Add to dashboard Cite

show abstract

“…By comparison, the side cameras in our method have independent optical systems, which can be inexpensive because of the low required resolution, and the whole system allows for much larger baselines. We also show that our super-resolution method is a significant improvement over that of [8]. Perhaps most similar to our concept is the upcoming commercial camera by Light 1 , which consists of an array of discrete cameras mounted on a common body.…”

Section: Introductionmentioning

confidence: 73%

“…The resolution improvement is, however, typically below a factor of 4×. To achieve a significant improvement, an additional highresolution reference image of the same scene is added as input; to this end, a hybrid imaging system [8] has been proposed based on a patch based synthesis strategy for synthesizing high-spatialresolution light field images with a scale factor of 8×. However, the quality of the recovered light field images is not as good as that of the input high-resolution image.…”

Section: Related Workmentioning

confidence: 99%

“…Similar to Boominathan et al [8], we synthesize a high-resolution light field from a high-quality 2D camera and a low-quality light fields. However, the light field camera considered in [8] is an integrated device (a Lytro camera), which increases the cost and limits the available baseline because all views share a main lens. By comparison, the side cameras in our method have independent optical systems, which can be inexpensive because of the low required resolution, and the whole system allows for much larger baselines.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Light Field Attachment: Turning a DSLR into a Light Field Camera Using a Low Budget Camera Ring

Wang

Liu

Heidrich

et al. 2017

IEEE Trans. Visual. Comput. Graphics

View full text Add to dashboard Cite

“…Light-fields offers immense flexibility in post-capture control including focus, aperture, and perspective changes. However, the direct methods of capturing light-fields [Levoy and Hanrahan 1996;Lytro ;Raytrix ] either trade off spatial resolution or require significant hardware changes/resources [Boominathan et al 2014] for the same sensor resolution and provide much limited aperture/focus control as compared to our framework. Several techniques have also been developed to reconstruct light-fields from focal or aperture stacks [Levin and Durand 2010;Green et al 2007]; as we show in our comparisons, we outperform all of these significantly in providing a finer control over depth-of-field (aperture) and focus.…”

Section: Hdr Imagingmentioning

confidence: 99%

Compressive epsilon photography for post-capture control in digital imaging

et al. 2014

Self Cite

View full text Add to dashboard Cite

A traditional camera requires the photographer to select the many parameters at capture time. While advances in light field photography have enabled post-capture control of focus and perspective, they suffer from several limitations including lower spatial resolution, need for hardware modifications, and restrictive choice of aperture and focus setting. In this paper, we propose "compressive epsilon photography," a technique for achieving complete postcapture control of focus and aperture in a traditional camera by acquiring a carefully selected set of 8 to 16 images and computationally reconstructing images corresponding to all other focus-aperture settings. We make the following contributions: first, we learn the statistical redundancies in focal-aperture stacks using a Gaussian Mixture Model; second, we derive a greedy sampling strategy for selecting the best focus-aperture settings; and third, we develop an algorithm for reconstructing the entire focal-aperture stack from a few captured images. As a consequence, only a burst of images with carefully selected camera settings are acquired. Post-capture, the user can then select any focal-aperture setting of choice and the corresponding image can be rendered using our algorithm. We show extensive results on several real data sets.

show abstract

Improving resolution and depth-of-field of light field cameras using a hybrid imaging system

Cited by 105 publications

References 29 publications

Non-Parametric Blur Map Regression for Depth of Field Extension

Non-Parametric Blur Map Regression for Depth of Field Extension

The Light Field Attachment: Turning a DSLR into a Light Field Camera Using a Low Budget Camera Ring

Compressive epsilon photography for post-capture control in digital imaging

Contact Info

Product

Resources

About