TU-D-204B-04: 4DCT Reconstruction from Undersampled Projections Using Edge-Preserving Total Variation and Non Local Means

In this paper, we propose acceleration methods for edge-preserving filtering. The filters natively include denormalized numbers, which are defined in IEEE Standard 754. The processing of the denormalized numbers has a higher computational cost than normal numbers; thus, the computational performance of edge-preserving filtering is severely diminished. We propose approaches to prevent the occurrence of the denormalized numbers for acceleration. Moreover, we verify an effective vectorization of the edge-preserving filtering based on changes in microarchitectures of central processing units by carefully treating kernel weights. The experimental results show that the proposed methods are up to five-times faster than the straightforward implementation of bilateral filtering and non-local means filtering, while the filters maintain the high accuracy. In addition, we showed effective vectorization for each central processing unit microarchitecture. The implementation of the bilateral filter is up to 14-times faster than that of OpenCV. The proposed methods and the vectorization are practical for real-time tasks such as image editing.

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Performance Assessment of Edge Preserving Filters

International Journal of Information System Modeling and Design ◽

10.4018/ijismd.2017040101 ◽

2017 ◽

Vol 8 (2) ◽

pp. 1-29

Author(s):

Kamireddy Rasool Reddy ◽

Madhava Rao Ch ◽

Nagi Reddy Kalikiri

Keyword(s):

Bilateral Filter ◽

Wavelet Thresholding ◽

Bilateral Filtering ◽

Noisy Image ◽

Edge Preserving ◽

Local Means ◽

Non Local ◽

Assess Quality ◽

Filter Algorithms ◽

Denoised Image

Denoising is one of the important aspects in image processing applications. Denoising is the process of eliminating the noise from the noisy image. In most cases, noise accumulates at the edges. So that prevention of noise at edges is one of the most prominent problem. There are numerous edge preserving approaches available to reduce the noise at edges in that Gaussian filter, bilateral filter and non-local means filtering are the popular approaches but in these approaches denoised image suffer from blurring. To overcome these problems, in this article a Gaussian/bilateral filtering (G/BF) with a wavelet thresholding approach is proposed for better image denoising. The performance of the proposed work is compared with some edge-preserving filter algorithms such as a bilateral filter and the Non-Local Means Filter, in terms that objectively assess quality. From the simulation results, it is found that the performance of proposed method is superior to the bilateral filter and the Non-Local Means Filter.

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Discrete Total Variation-Based Non-Local Means Filter for Denoising Magnetic Resonance Images

Journal of Information Technology Research ◽

10.4018/jitr.2020100102 ◽

2020 ◽

Vol 13 (4) ◽

pp. 14-31

Author(s):

Nikita Joshi ◽

Sarika Jain ◽

Amit Agarwal

Keyword(s):

Magnetic Resonance ◽

Total Variation ◽

Similarity Index ◽

Noise Removal ◽

Magnetic Resonance Images ◽

Variation Method ◽

Mr Images ◽

Denoising Method ◽

Local Means ◽

Non Local

Magnetic resonance (MR) images suffer from noise introduced by various sources. Due to this noise, diagnosis remains inaccurate. Thus, removal of noise becomes a very important task when dealing with MR images. In this paper, a denoising method has been discussed that makes use of non-local means filter and discrete total variation method. The proposed approach has been compared with other noise removal techniques like non-local means filter, anisotropic diffusion, total variation, and discrete total variation method, and it proves to be effective in reducing noise. The performance of various denoising methods is compared on basis of metrics such as peak signal-to-noise ratio (PSNR), mean square error (MSE), universal image quality index (UQI), and structure similarity index (SSIM) values. This method has been tested for various noise levels, and it outperformed other existing noise removal techniques, without blurring the image.

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