Wensen Feng, Yunjin Chen
The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this study we aim to propose an […]
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Mirko Myllykoski
This dissertation focuses on block cyclic reduction (BCR) type fast direct solvers, graphics processing unit (GPU) computation, and image denoising. The fast direct solvers are specialized methods for solving certain types of linear systems. They take into account specific characteristics of the system and are therefore able to solve the system much more efficiently than […]
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Yunjin Chen, Thomas Pock
Image restoration is a long-standing problem in low-level computer vision with many interesting applications. We describe a flexible learning framework to obtain simple but effective models for various image restoration problems. The proposed approach is based on the concept of nonlinear reaction diffusion, but we extend conventional nonlinear reaction diffusion models by highly parametrized linear […]
Sagar Venkatesh Gubbi, Chandra Sekhar Seelamantula
Image denoising is a classical problem in image processing and has applications in areas ranging from photography to medical imaging. In this paper, we examine the denoising performance of an optimized spatially-varying Gaussian filter. The parameters of the Gaussian filter are tuned by optimizing a mean squared error estimate which is similar Stein’s Unbiased Risk […]
Mario Mastriani
A quantum Boolean image processing methodology is presented in this work, with special emphasis in image denoising. A new approach for internal image representation is outlined together with two new interfaces: classical-to-quantum and quantum-to-classical. The new quantum-Boolean image denoising called quantum Boolean mean filter (QBMF) works with computational basis states (CBS), exclusively. To achieve this, […]
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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. […]
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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 […]
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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 […]
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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 […]
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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. […]
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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 […]
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