12387
Maria G. Sanchez, Vicente Vidal, Josep Arnal, Anna Vidal
A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. […]
View View   Download Download (PDF)   
Anders Eklund, Paul Dufort
We have presented solutions for fast non-separable floating point convolution in 2, 3 and 4 dimensions, using the CUDA programming language. We believe that these implementations will serve as a complement to the NPP library, which currently only supports 2D filters and images stored as integers. The shared memory implementation with loop unrolling is approximately […]
Giuseppe Palma, Francesco Piccialli, Pasquale De Michele, Salvatore Cuomo, Marco Comerci, Pasquale Borrelli, Bruno Alfano
Non-Local Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. High computational complexity led to implementations on Graphic Processor Unit (GPU) architectures, which achieve reasonable running times by filtering, slice-by-slice, 3D datasets with a 2D NLM approach. Here we present a fully 3D NLM implementation on a multi-GPU architecture […]
View View   Download Download (PDF)   
Tejaswi Agarwal, Saurabh Jha, B. Rajesh Kanna
This paper presents a parallel Salt and Pepper (SP) noise removal algorithm in a grey level digital image based on the Hypergraph Based Root Mean Square (HGRMS) approach. HGRMS is generic algorithm for identifying noisy pixels in any digital image using a two level hierarchical serial approach. However, for SP noise removal, we reduce this […]
View View   Download Download (PDF)   
M. G. Sanchez, V. Vidal, J. Bataller, G. Verdu
In this paper, we present an efficient implementation of parallel algorithms to remove noise in digital images using different Graphics Processing Units (GPUs). The algorithm, based on the concept of peer group, uses a fuzzy metric for finding wrong pixels and the Arithmetic Mean Filter (AMF) to correct it. There are many factors to study […]
View View   Download Download (PDF)   
M. Aldinucci, C. Spampinato, M. Drocco, M. Torquati, S. Palazzo
In this paper a two-phase filter for removing "salt and pepper" noise is proposed. In the first phase, an adaptive median filter is used to identify the set of the noisy pixels; in the second phase, these pixels are restored according to a regularization method, which contains a data-fidelity term reflecting the impulse noise characteristics. […]
View View   Download Download (PDF)   
Ziyi Zheng, Wei Xu and Klaus Mueller
Neighborhood denoising filters are powerful techniques in image processing and can effectively enhance the image quality in CT reconstructions. In this study, by taking the bilateral filter and the non-local mean filter as two examples, we discuss their implementations and perform fine-tuning on the targeted GPU architecture. Experimental results show that the straightforward GPU-based neighborhood […]
View View   Download Download (PDF)   
Jan Horacek, Jan Kolomaznik, Josef Pelikan, Martin Horak
Volumetric data is currently gradually being used more and more in everyday aspect of our lives. Processing such data is computationally expensive and until now more sophisticated algorithms could not be used. The possibilities of processing such data have considerably widened since the increase of parallel computational power in modern GPUs. We present a novel […]
View View   Download Download (PDF)   
Benjamin Huhle, Timo Schairer, Philipp Jenke, Wolfgang Strasser
We give a brief discussion of denoising algorithms for depth data and introduce a novel technique based on the NL-means filter. A unified approach is presented that removes outliers from depth data and accordingly achieves an unbiased smoothing result. This robust denoising algorithm takes intra-patch similarity and optional color information into account in order to […]
View View   Download Download (PDF)   
Yang Su, Zhijie Xu
The discrete wavelet transform (DWT) has been extensively used for image compression and denoising in the areas of image processing and computer vision. However, the intensive computation of DWT due to its inherent multilevel data decomposition and reconstruction operations brings a bottleneck that drastically reduces its performance and implementations for real-time applications when facing large […]
View View   Download Download (PDF)   
Yang Su, Zhi-Jie Xu, Xiang-Qian Jiang
Abstract The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and wavelet-based image denoising algorithms […]
View View   Download Download (PDF)   

* * *

* * *

Like us on Facebook

HGPU group

128 people like HGPU on Facebook

Follow us on Twitter

HGPU group

1193 peoples are following HGPU @twitter

Featured events

* * *

Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

The information send to hgpu.org will be treated according to our Privacy Policy

HGPU group © 2010-2014 hgpu.org

All rights belong to the respective authors

Contact us: