Random coded sampling for high-speed HDR video

Portz, Travis; Zhang, Li; Jiang, Hongrui

doi:10.1109/iccphot.2013.6528308

Cited by 15 publications

(8 citation statements)

References 18 publications

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“…Holloway et al used temporal modulation only (i.e., a flutter shutter) to recover compressive video using only fastspeed switching modes on a commodity sensor [19]. Such switching modes have also proven successful in capturing focal stacks [20] or high-speed high dynamic range video [21]. In a recent Nature publication, a static mask pattern displayed on an SLM to reconstruct 10 picosecond resolution video of non-periodic events was demonstrated [22].…”

Section: Related Workmentioning

confidence: 99%

High spatio-temporal resolution video with compressed sensing

et al. 2015

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We present a prototype compressive video camera that encodes scene movement using a translated binary photomask in the optical path. The encoded recording can then be used to reconstruct multiple output frames from each captured image, effectively synthesizing high speed video. The use of a printed binary mask allows reconstruction at higher spatial resolutions than has been previously demonstrated. In addition, we improve upon previous work by investigating tradeoffs in mask design and reconstruction algorithm selection. We identify a mask design that consistently provides the best performance across multiple reconstruction strategies in simulation, and verify it with our prototype hardware. Finally, we compare reconstruction algorithms and identify the best choice in terms of balancing reconstruction quality and speed.

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Section: Related Workmentioning

confidence: 99%

High spatio-temporal resolution video with compressed sensing

et al. 2015

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“…Other works rely on multiple cameras [SBB14, BRG*14], enhanced sensor control electronics performed in simulation [PZJ13], or otherwise highly modified hardware designs [MRK*13, ZSFC*15]. For instance, Tocci et al [TKTS11] and Kronander et al [KGBU13] achieve single‐shot HDR by acquiring several LDR images with different sensors using a beam splitter.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Sparse Coding for High Dynamic Range Imaging

Serrano

Heide

Gutiérrez

et al. 2016

Computer Graphics Forum

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Figure 1: High dynamic range image (HDRI) recovered from a single, coded, 8-bit low dynamic range (LDR) image using the proposed sparse reconstruction method. Left: HDR image recovered with our framework, tonemapped for display purposes. The inset shows a cropped region of the coded LDR image used as input to the reconstruction algorithm. Center left: Close-up of two exposures of the reconstructed HDR image showing the ability of our method to reconstruct an extended dynamic range. Center right: normalized luminance plots of the marked scanline (yellow line, rotated by 90• ) for the reconstructed image (green curve) and the ground truth image (blue curve). Right: false color image of the reconstructed HDR scene (scale is in stops), showing the extremely large dynamic range that the original scene had and our technique is able recover. AbstractCurrent HDR acquisition techniques are based on either (i) fusing multibracketed, low dynamic range (LDR) images, (ii) modifying existing hardware and capturing different exposures simultaneously with multiple sensors, or (iii) reconstructing a single image with spatially-varying pixel exposures. In this paper, we propose a novel algorithm to recover high-quality HDRI images from a single, coded exposure. The proposed reconstruction method builds on recently-introduced ideas of convolutional sparse coding (CSC); this paper demonstrates how to make CSC practical for HDR imaging. We demonstrate that the proposed algorithm achieves higher-quality reconstructions than alternative methods, we evaluate optical coding schemes, analyze algorithmic parameters, and build a prototype coded HDR camera that demonstrates the utility of convolutional sparse HDRI coding with a custom hardware platform.

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“…Although these methods use several cameras, they only increase the temporal (and spatial) resolution of regular videos, while we increase the temporal resolution and capture HDR light-field videos. Portz et al [PZJ13] propose a random per-pixel exposure coding for a hypothetical sensor. Temporal upsampling and HDR is achieved by finding similar space-time blocks for each pixel throughout an entire video sequence.…”

Section: Related Workmentioning

confidence: 99%

Coded exposure HDR light‐field video recording

Schedl

Birklbauer

Bimber

2014

Computer Graphics Forum

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Figure 1: Wide-aperture renderings of a HDR light-field video sequence recorded during a left-to-right motion of a camera array. Focus, aperture, perspective, and exposure of each individual frame can be altered after recording. The example shows a successive increase of the exposure time with a constant foreground focus, and a focus shift to the background at the longest exposure. Common exposure sequencing (top) leads to strong motion blur that is significantly reduced with our coded exposure approach (bottom). Note that motion blur increases with exposure time and parallax. Close objects are therefore more blurred at longer exposure times than distant objects at shorter exposures. The stripe pattern in out-of-focus regions is the result of directional under-sampling and is reduced with a larger number of cameras and more advanced light-field interpolation. AbstractCapturing exposure sequences to compute high dynamic range (HDR) images causes motion blur in cases of camera movement. This also applies to light-field cameras: frames rendered from multiple blurred HDR lightfield perspectives are also blurred. While the recording times of exposure sequences cannot be reduced for a single-sensor camera, we demonstrate how this can be achieved for a camera array. Thus, we decrease capturing time and reduce motion blur for HDR light-field video recording. Applying a spatio-temporal exposure pattern while capturing frames with a camera array reduces the overall recording time and enables the estimation of camera movement within one light-field video frame. By estimating depth maps and local point spread functions (PSFs) from multiple perspectives with the same exposure, regional motion deblurring can be supported. Missing exposures at various perspectives are then interpolated.

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Random coded sampling for high-speed HDR video

Cited by 15 publications

References 18 publications

High spatio-temporal resolution video with compressed sensing

High spatio-temporal resolution video with compressed sensing

Convolutional Sparse Coding for High Dynamic Range Imaging

Coded exposure HDR light‐field video recording

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