GPU-Based Edge-Directed Image Interpolation

Kraus, Martin; Eissele, Mike; Strengert, Magnus

doi:10.1007/978-3-540-73040-8_54

Cited by 18 publications

(11 citation statements)

References 10 publications

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“…[1] suggest a value of m ≈ 0.25, which worked fine in our experiments. To avoid strong edge sharpening in rather homogeneous regions, we also introduced a lower threshold t g .…”

Section: Enhancement Of Jumping Edgessupporting

confidence: 70%

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Sub-pixel data fusion and edge-enhanced distance refinement for 2D/3D images

Lindner

Lambers

Kolb

2008

IJISTA

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An important field of reasearch in computer vision is the 3D analysis and reconstruction of objects and scenes. A rather new technologie in this context is the Photonic Mixer Device (PMD), based on the time-of-flight principle, which measures full-range distance information in real-time. Unfortunately, PMD-based devices have still limited resolution and provide only IR intensity information.This paper describes a fast algorithmic approach to combine high resolution RGB images with PMD distance data, acquired using a binocular camera setup. The resulting combined RGBZ-data not only enhances the visual result, but also represents a basis for advanced data processing in e. g. object recognition with sub-pixel accuracy. A simple but efficient method is used to detect geometric occlusion caused by the binocular setup, which otherwise will lead to false color assignments.Additionally, we introduce an enhanced filtering technique used for the edge-enhanced distance refinement of the geometry provided by the PMD camera. The technique incorporates a proper handling of boundaries and an iterative refinement approach, which can be used to enhance the 2D/3D-fusion accuracy.

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“…[1] suggest a value of m ≈ 0.25, which worked fine in our experiments. To avoid strong edge sharpening in rather homogeneous regions, we also introduced a lower threshold t g .…”

Section: Enhancement Of Jumping Edgessupporting

confidence: 70%

“…[1]. Gradient-based resampling techniques are used for upsampling RGB images, preserving sharp edges in the upscaled image version.…”

Section: Enhancement Of Jumping Edgesmentioning

confidence: 99%

Sub-pixel data fusion and edge-enhanced distance refinement for 2D/3D images

Lindner

Lambers

Kolb

2008

IJISTA

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“…Ljung et al (2006) therefore performed interblock interpolation on the GPU to improve the quality of the visualization. Kraus et al (2007) implemented edge-directed image interpolation with GPU acceleration, to achieve upsampling in real-time without ringing artefacts. Similar work was presented by Cao et al (2009).…”

Section: Interpolationmentioning

confidence: 99%

Medical image processing on the GPU – Past, present and future

Eklund

Dufort

Forsberg

et al. 2013

Medical Image Analysis

347

198

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Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient. In the field of medical imaging, GPUs are in some cases crucial for enabling practical use of computationally demanding algorithms. This review presents the past and present work on GPU accelerated medical image processing, and is meant to serve as an overview and introduction to existing GPU implementations. The review covers GPU acceleration of basic image processing operations (filtering, interpolation, histogram estimation and distance transforms), the most commonly used algorithms in medical imaging (image registration, image segmentation and image denoising) and algorithms that are specific to individual modalities (CT, PET, SPECT, MRI, fMRI, DTI, ultrasound, optical imaging and microscopy). The review ends by highlighting some future possibilities and challenges.

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“…The second problem is addressed by deblurring of the output using median filter. GPU based efficient solutions for data parallel image processing applications have been proposed by many authors [5][6] [7] [8]. Like many image processing applications, Steering Kernel Regression also has inherent data parallelism.…”

Section: Introductionmentioning

confidence: 99%

Median Based Parallel Steering Kernel Regression for Image Reconstruction

Khalandhar¹,

Ram²,

Rao³

et al. 2013

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Image reconstruction is a process of obtaining the original image from corrupted data. Applications of image reconstruction include Computer Tomography, radar imaging, weather forecasting etc. Recently steering kernel regression method has been applied for image reconstruction [1]. There are two major drawbacks in this technique. Firstly, it is computationally intensive. Secondly, output of the algorithm suffers form spurious edges (especially in case of denoising). We propose a modified version of Steering Kernel Regression called as Median Based Parallel Steering Kernel Regression Technique. In the proposed algorithm the first problem is overcome by implementing it in on GPUs and multi-cores. The second problem is addressed by a gradient based suppression in which median filter is used.Our algorithm gives better output than that of the Steering Kernel Regression. The results are compared using Root Mean Square Error(RMSE). Our algorithm has also shown a speedup of 21x using GPUs and shown speedup of 6x using multi-cores.

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GPU-Based Edge-Directed Image Interpolation

Cited by 18 publications

References 10 publications

Sub-pixel data fusion and edge-enhanced distance refinement for 2D/3D images

Sub-pixel data fusion and edge-enhanced distance refinement for 2D/3D images

Medical image processing on the GPU – Past, present and future

Median Based Parallel Steering Kernel Regression for Image Reconstruction

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