scholarly journals An enhanced adaptive non-local means algorithm for Rician noise reduction in magnetic resonance brain images

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Kaixin Chen ◽  
Xiao Lin ◽  
Xing Hu ◽  
Jiayao Wang ◽  
Han Zhong ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Experimental Results ◽  
Rician Noise ◽  
Search Window ◽  
Fixed Size ◽  
Fcm Algorithm ◽  
Fuzzy C Means ◽  
Local Means ◽  
Non Local ◽  
Image Details

Abstract Background The Rician noise formed in magnetic resonance (MR) imaging greatly reduced the accuracy and reliability of subsequent analysis, and most of the existing denoising methods are suitable for Gaussian noise rather than Rician noise. Aiming to solve this problem, we proposed fuzzy c-means and adaptive non-local means (FANLM), which combined the adaptive non-local means (NLM) with fuzzy c-means (FCM), as a novel method to reduce noise in the study. Method The algorithm chose the optimal size of search window automatically based on the noise variance which was estimated by the improved estimator of the median absolute deviation (MAD) for Rician noise. Meanwhile, it solved the problem that the traditional NLM algorithm had to use a fixed size of search window. Considering the distribution characteristics for each pixel, we designed three types of search window sizes as large, medium and small instead of using a fixed size. In addition, the combination with the FCM algorithm helped to achieve better denoising effect since the improved the FCM algorithm divided the membership degrees of images and introduced the morphological reconstruction to preserve the image details. Results The experimental results showed that the proposed algorithm (FANLM) can effectively remove the noise. Moreover, it had the highest peak signal-noise ratio (PSNR) and structural similarity (SSIM), compared with other three methods: non-local means (NLM), linear minimum mean square error (LMMSE) and undecimated wavelet transform (UWT). Using the FANLM method, the image details can be well preserved with the noise being mostly removed. Conclusion Compared with the traditional denoising methods, the experimental results showed that the proposed approach effectively suppressed the noise and the edge details were well retained. However, the FANLM method took an average of 13 s throughout the experiment, and its computational cost was not the shortest. Addressing these can be part of our future research.

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.

scholarly journals Brain MR image denoising for Rician noise using pre-smooth non-local means filter

2015 ◽  
Vol 14 (1) ◽  
pp. 2 ◽  
Author(s):  
Jian Yang ◽  
Jingfan Fan ◽  
Danni Ai ◽  
Shoujun Zhou ◽  
Songyuan Tang ◽  
...  
Keyword(s):  
Image Denoising ◽  
Rician Noise ◽  
Mr Image ◽  
Local Means ◽  
Non Local

Fuzzy similarity based non local means filter for Rician noise removal

2014 ◽  
Vol 74 (15) ◽  
pp. 5533-5556 ◽  
Author(s):  
Muhammad Sharif ◽  
Ayyaz Hussain ◽  
Muhammad Arfan Jaffar ◽  
Tae-Sun Choi
Keyword(s):  
Noise Removal ◽  
Rician Noise ◽  
Local Means ◽  
Fuzzy Similarity ◽  
Non Local

scholarly journals Optimal graph edge weights driven nlms with multi-layer residual compensation

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Fang Yang ◽  
Xin Chen ◽  
Li Chai
Keyword(s):  
Image Denoising ◽  
Graph Laplacian ◽  
Denoising Method ◽  
Local Means ◽  
Effective Performance ◽  
Non Local ◽  
Local Gradient ◽  
Laplacian Regularization ◽  
Image Details ◽  
Optimal Graph

AbstractNon-local Means (NLMs) play essential roles in image denoising, restoration, inpainting, etc., due to its simple theory but effective performance. However, when the noise increases, the denoising accuracy of NLMs decreases significantly. This paper further develop the NLMs-based denoising method to remove noise with less loss of image details. It is realized by embedding an optimal graph edge weights driven NLMs kernel into a multi-layer residual compensation framework. Unlike the patch similarity-based weights in the traditional NLMs filters, the edge weights derived from the optimal graph Laplacian regularization consider (1) the distance between the target pixel and the candidate pixel, (2) the local gradient and (3) the patch similarity. After defining the weights, the graph-based NLMs kernel is then put into a multi-layer framework. The corresponding primal and residual terms at each layer are finally fused with learned weights to recover the image. Experimental results show that our method is effective and robust, especially for piecewise smooth images.

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