Latent common origin of bilateral filter and non-local means filter

2010 ◽  
Author(s):  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Bilateral Filter ◽  
Common Origin ◽  
Local Means ◽  
Non Local

scholarly journals Monte-Carlo Acceleration of Bilateral Filter and Non-Local Means

2018 ◽  
Vol 27 (3) ◽  
pp. 1462-1474 ◽  
Author(s):  
Christina Karam ◽  
Keigo Hirakawa
Keyword(s):  
Monte Carlo ◽  
Bilateral Filter ◽  
Local Means ◽  
Non Local

scholarly journals Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures

2018 ◽  
Vol 8 (10) ◽  
pp. 1985 ◽  
Author(s):  
Yoshihiro Maeda ◽  
Norishige Fukushima ◽  
Hiroshi Matsuo
Keyword(s):  
Computational Cost ◽  
Bilateral Filter ◽  
Processing Unit ◽  
Normal Numbers ◽  
Edge Preserving ◽  
Central Processing ◽  
Local Means ◽  
Kernel Weights ◽  
Computational Performance ◽  
Non Local

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.

Performance Assessment of Edge Preserving Filters

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.

scholarly journals Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

2021 ◽  
Vol 18 (6) ◽  
pp. 2903
Author(s):  
Seong-Hyeon Kang ◽  
Ji-Youn Kim
Keyword(s):  
Light Microscopy ◽  
Noise Reduction ◽  
Natural Image ◽  
Search Window ◽  
Local Means ◽  
Image Characteristics ◽  
Non Local ◽  
Spatial Quality ◽  
Evaluation Parameters ◽  
Color Channels

The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

Automated Image Denoising Model: Contribution Towards Optimized Internal and External Basis

2021 ◽  
pp. 2150051
Author(s):  
S. Elavaar Kuzhali ◽  
D. S. Suresh
Keyword(s):  
Image Denoising ◽  
Image Patch ◽  
The Past ◽  
Local Means ◽  
Hybrid Optimization Algorithm ◽  
Proposed Model ◽  
Processing Step ◽  
Non Local ◽  
Chicken Swarm Optimization ◽  
Selection Of

For handling digital images for various applications, image denoising is considered as a fundamental pre-processing step. Diverse image denoising algorithms have been introduced in the past few decades. The main intent of this proposal is to develop an effective image denoising model on the basis of internal and external patches. This model adopts Non-local means (NLM) for performing the denoising, which uses redundant information of the image in pixel or spatial domain to reduce the noise. While performing the image denoising using NLM, “denoising an image patch using the other noisy patches within the noisy image is done for internal denoising and denoising a patch using the external clean natural patches is done for external denoising”. Here, the selection of optimal block from the entire datasets including internal noisy images and external clean natural images is decided by a new hybrid optimization algorithm. The two renowned optimization algorithms Chicken Swarm Optimization (CSO), and Dragon Fly Algorithm (DA) are merged, and the new hybrid algorithm Rooster-based Levy Updated DA (RLU-DA) is adopted. The experimental results in terms of some relevant performance measures show the promising results of the proposed model with remarkable stability and high accuracy.

Recursively adaptive perceptual non-local means for video coding

2021 ◽  
Author(s):  
Xinjie Luo ◽  
Ju Huang ◽  
Guoqing Xiang ◽  
Hongfei Fan ◽  
Yi Hou ◽  
...  
Keyword(s):  
Video Coding ◽  
Local Means ◽  
Non Local
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