Near Real-Time Estimation of Super-Resolved Depth and All-In-Focus Images from a Plenoptic Camera Using Graphics Processing Units

Luke, J. P.; Nava, Fernando Pérez; Marichal-Hernández, José G.; Rodríguez-Ramos, José Manuel; Rosa, F.

doi:10.1155/2010/942037

Cited by 12 publications

(13 citation statements)

References 17 publications

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“…We propose a new parallelization technique to speed up the message computation, which takes up the longest time in BP. As a result, we obtained higher performance than the previous works [6][7][8] on CUDA implementation of BP.…”

Section: Introductionmentioning

confidence: 65%

“…The approach taken in [8] can be used to mitigate this problem. Rather than using one thread per pixel as in [7], they proposed utilizing multiple threads per pixel.…”

Section: ) Previous Cuda Implementationmentioning

confidence: 99%

“…Thus, simply averaging the mean error does not accurately reflect the error performance of the algorithm. Relative mean error [14] is used to take this effect into account: (8) where g is the estimated ground truth, d is the ground truth, and n is the number of non-occluded pixels. We exclude pixels far away (g i = 0).…”

Section: B Distance Errormentioning

confidence: 99%

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CUDA implementation of belief propagation for stereo vision

Choi

2010

13th International IEEE Conference on Intelligent Transportation Systems

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Measuring distance to obstacles is an important process for intelligent vehicles (IV). With accurate measurement, IV can make appropriate maneuver to avoid such obstacles. To obtain highly accurate result, we used a Markov random field model-based global energy minimization algorithm called belief propagation (BP). However, BP has high computational complexity which makes it difficult for real-time processing. To solve this issue, we took massively parallel approach using Compute Unified Device Architecture (CUDA). In this paper, we first provide profiling result to find the performance bottleneck of BP. Next, we explain CUDA-specific optimization techniques to enhance the performance. We propose a new parallelization technique to speed up the message computation, which takes up the longest time in BP. The experimental result shows that we were able to obtain accurate distance estimation result in real time.

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Section: Introductionmentioning

confidence: 65%

“…The approach taken in [8] can be used to mitigate this problem. Rather than using one thread per pixel as in [7], they proposed utilizing multiple threads per pixel.…”

Section: ) Previous Cuda Implementationmentioning

confidence: 99%

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CUDA implementation of belief propagation for stereo vision

Choi

2010

13th International IEEE Conference on Intelligent Transportation Systems

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“…[63][64][65] Thus, focusing metrics based on the quantification of image sharpness are applied by searching on the reconstructed images for the planes that contain the objects with the peak and sharpest details to establish the all-in-focus profile and its corresponding depth map. 66 At each pixel, the intensity variation in longitudinal direction is investigated to find out the peak intensity. The peak intensity value becomes a pixel of an all-in-focus profile and its location in longitudinal direction is recorded as a corresponding depth map value.…”

Section: Quantification Of Image Sharpness and Peak Searchingmentioning

confidence: 99%

Review of digital holographic microscopy for three-dimensional profiling and tracking

et al. 2014

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Digital holographic microscopy (DHM) is a potent tool to perform three-dimensional imaging and tracking. We present a review of the state-of-the-art of DHM for three-dimensional profiling and tracking with emphasis on DHM techniques, reconstruction criteria for three-dimensional profiling and tracking, and their applications in various branches of science, including biomedical microscopy, particle imaging velocimetry, micrometrology, and holographic tomography, to name but a few. First, several representative DHM configurations are summarized and brief descriptions of DHM processes are given. Then we describe and compare the reconstruction criteria to obtain three-dimensional profiles and four-dimensional trajectories of objects. Details of the simulated and experimental evidences of DHM techniques and related reconstruction algorithms on particles, biological cells, fibers, etc., with different shapes, sizes, and conditions are also provided. The review concludes with a summary of techniques and applications of three-dimensional imaging and four-dimensional tracking by DHM.

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“…To compute the photography operator at a certain depth , we need to compute a line integral on the light field and compute the integral one usually needs to interpolate the values that are not available, since a plenoptic camera captures discrete samples of the light field. Instead, we use the Super-resolution Discrete Focal Stack Transform (SDFST) developed in [12] and [13], based on performing the transform for a set of lines with slopes that pass through points in the discretized light field (see Fig. 4).…”

Section: Plenoptic Sensor and Super Resolution Algorithmmentioning

confidence: 99%

Design and Laboratory Results of a Plenoptic Objective: From 2D to 3D With a Standard Camera

Montilla

Puga

Luke

et al. 2015

J. Display Technol.

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The plenoptic camera was originally created to allow the capture of the light field, a four-variable volume representation of all rays and their directions, which allows the creation by synthesis of an image of the observed object. This method has several advantages with regard to 3D capture systems based on stereo cameras since it does not need frame synchronization or geometric and color calibration. It also has many applications, from 3DTV to medical imaging. A plenoptic camera uses a microlens array to measure the radiance and direction of all the light rays in a scene. The array is placed at a distance from the principal lens, which is conjugated to the distance where the scene is situated, and the sensor is at the focal plane of the microlenses. We have designed a plenoptic objective that incorporates a microlens array and a relay system that reimages the microlens plane. This novel approach has proven successful. Placing it on a camera, the plenoptic objective creates a virtual microlens plane in front of the camera CCD, allowing it to capture the light field of the scene. In this paper we present the experimental results showing that depth information is perfectly captured when using an external plenoptic objective. Using this objective transforms any camera into a 3D sensor, opening up a wide range of applications from microscopy to astronomy.

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Near Real-Time Estimation of Super-Resolved Depth and All-In-Focus Images from a Plenoptic Camera Using Graphics Processing Units

Cited by 12 publications

References 17 publications

CUDA implementation of belief propagation for stereo vision

CUDA implementation of belief propagation for stereo vision

Review of digital holographic microscopy for three-dimensional profiling and tracking

Design and Laboratory Results of a Plenoptic Objective: From 2D to 3D With a Standard Camera

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