Acceleration techniques and evaluation on multi-core CPU, GPU and FPGA for image processing and super-resolution

Georgis, Georgios; Lentaris, George; Reisis, D.

doi:10.1007/s11554-016-0619-6

Cited by 32 publications

(16 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically, our stereo matcher concerns bidirectional disparity map generation with 7 × 7 Gauss-weighted cost aggregation, and its architecture is detailed in [72]. For one pair of 1120 × 1120 8-bit pixel images with 200 disparity levels, a four-engine parallelization on Virtex6 VLX240T requires 0.54 s. Therefore, the FPGA is 29x faster than Intel i5 (see [89] for detailed comparison) and approximately 7700x faster than space-grade LEON3; this factor was derived after extrapolation from smaller images due to memory limitations on LEON3. Such huge factors can be also derived for Harris detection and scale-invariant feature transform (SIFT) description.…”

Section: Field-programmable Gate Arraymentioning

confidence: 99%

“…Besides Harris, we also use image superresolution (SR) and stereo matching (disparity [72]) to compare desktop GPUs to FPGAs. We tested a variety of devices and performed in-depth optimization of the implemented benchmarks on both platforms; a detailed analysis has been published separately [89]. Here, we consider Nvidia GTX 670/960, whereas for the FPGA (Zynq7000 Artix), we consider both processing and communication time, in contrast to the study in [89] that focuses on processing.…”

Section: Graphics Processing Unitmentioning

confidence: 99%

See 1 more Smart Citation

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

Lentaris¹,

Maragos²,

Stratakos³

et al. 2018

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

Vision-based navigation has become increasingly important in a variety of space applications for enhancing autonomy and dependability. Future missions, such as active debris removal for remediating the low Earth orbit environment, will rely on novel high-performance avionics to support advanced image processing algorithms with substantial workloads. However, when designing new avionics architectures, constraints relating to the use of electronics in space present great challenges, further exacerbated by the need for significantly faster processing compared to conventional space-grade central processing units. With the long-term goal of designing highperformance embedded computers for space, in this paper, an extended study and tradeoff analysis of a diverse set of computing platforms and architectures (i.e., central processing units, multicore digital signal processors, graphics processing units, and field-programmable gate arrays) are performed with radiation-hardened and commercial offthe-shelf technology. Overall, the study involves more than 30 devices and 10 benchmarks, which are selected after exploring the algorithms and specifications required for vision-based navigation. The present analysis combines literature survey and in-house development/testing to derive a sizable consistent picture of all possible solutions. Among others, the results show that certain 28 nm system-on-chip devices perform faster than space-grade and embedded central processing units by 1-3 orders of magnitude, while consuming less than 10 W. Field-programmable gate array platforms provide the highest performance per watt ratio.

show abstract

Section: Field-programmable Gate Arraymentioning

confidence: 99%

Section: Graphics Processing Unitmentioning

confidence: 99%

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

Lentaris¹,

Maragos²,

Stratakos³

et al. 2018

Journal of Aerospace Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…Assuming that the motion of adjacent frames is uniformly varying, according to Equations (4) and (5), the dynamic description model A and the observation model H are given by…”

Section: Motion Trajectory Filtering Based On Kalman Filtermentioning

confidence: 99%

“…Through the image stabilization technology, the impact of the moving camera on the image can be eliminated or reduced, which greatly improves the quality of image sequences. In addition, with the development of large area scientific imaging arrays, as well as the high speed processing elements such as DSP (digital signal processing), FPGA (field programmable gate array), GPU (graphics processing unit) and CUDA (compute unified device architecture), real-time image processing technology for high resolution images has become a hot topic in recent years [4,5], while real-time electronic image stabilization technology as one of the significant image processing techniques has also been extensively studied.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching

et al. 2020

View full text Add to dashboard Cite

The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.

show abstract

“…In this context, the performance of multi-core CPUs, GPUs and FPGAs have been evaluated in multiple application domain like image processing [17] and computer graphics [18]. As opposed to this approach, HCS evaluate performance results in terms of cooperative work across computing units.…”

Section: Introductionmentioning

confidence: 99%

High-Performance Computation of Bézier Surfaces on Parallel and Heterogeneous Platforms

Palomar

Gómez-Luna

Cheikh³

et al. 2017

Int J Parallel Prog

View full text Add to dashboard Cite

Bézier surfaces are mathematical tools employed in a wide variety of applications. Some works in the literature propose parallelization strategies to improve performance for the computation of Bézier surfaces. These approaches, however, are mainly focused on graphics applications and often are not directly applicable to other domains. In this work, we propose a new method for the computation of Bézier surfaces, together with approaches to efficiently map the method onto different platforms (CPUs, discrete and integrated GPUs). Additionally, we explore CPU-GPU cooperation mechanisms for computing Bézier surfaces using two integrated heterogeneous systems with different characteristics. An exhaustive performance evaluation-including different data-types, rendering and several hardware platforms-is performed. The results show that our method achieves speedups as high as 3.12x (double-precision) and 2.47x (single-precision) on CPU, and 3.69x (double-precision) and 13.14x (single-precision) on GPU compared to other methods in the literature. In heterogeneous platforms, the CPU-GPU cooperation increases the performance up to 2.09x with respect to the GPU-only version. Our method and the 123Int J Parallel Prog associated parallelization approaches can be easily employed in domains other than computer-graphics (e.g., image registration, bio-mechanical modeling and flow simulation), and extended to other Bézier formulations and Bézier constructions of higher order than surfaces.

show abstract

Acceleration techniques and evaluation on multi-core CPU, GPU and FPGA for image processing and super-resolution

Cited by 32 publications

References 43 publications

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

High-Performance Embedded Computing in Space: Evaluation of Platforms for Vision-Based Navigation

Real-Time Image Stabilization Method Based on Optical Flow and Binary Point Feature Matching

High-Performance Computation of Bézier Surfaces on Parallel and Heterogeneous Platforms

Contact Info

Product

Resources

About