Median non-local means filtering for low SNR image denoising: Application to PET with anatomical knowledge

Chan, Chung; Fulton, Roger; Feng, David Dagan; Meikle, Steven R.

doi:10.1109/nssmic.2010.5874485

Cited by 25 publications

(9 citation statements)

References 21 publications

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“…Zhong et al [69] combined the NL-Means with Lee filter [36] for SAR image enhancing. Chan et al [7] incorporated a median filtering operation indirectly in the NL-Means method for denoising low signal noise ratio (SNR) images. Maruf and El-Sakka [43] projected NL-Means patches into a global feature space before performing a statistical t test to reduce the dimensionality of this feature space.…”

Section: Averaging Patch-based: Non-local Meansmentioning

confidence: 99%

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Alkinani

El-Sakka

2017

J Image Video Proc.

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Background: Digital images are captured using sensors during the data acquisition phase, where they are often contaminated by noise (an undesired random signal). Such noise can also be produced during transmission or by poor-quality lossy image compression. Reducing the noise and enhancing the images are considered the central process to all other digital image processing tasks. The improvement in the performance of image denoising methods would contribute greatly on the results of other image processing techniques. Patch-based denoising methods recently have merged as the state-of-the-art denoising approaches for various additive noise levels. In this work, the use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated. Various types of image datasets are addressed to conduct this study. Methods: We first explain the type of noise in digital images and discuss various image denoising approaches, with a focus on patch-based denoising methods. Then, we experimentally evaluate both quantitatively and qualitatively the patch-based denoising methods. The patch-based image denoising methods are analyzed in terms of quality and computational time. Results: Despite the sophistication of patch-based image denoising approaches, most patch-based image denoising methods outperform the rest. Fast patch similarity measurements produce fast patch-based image denoising methods. Conclusion: Patch-based image denoising approaches can effectively reduce noise and enhance images. Patch-based image denoising approach is the state-of-the-art image denoising approach.Keywords: Patch-based image denoising, Bilateral filter, Non-local means filtering, Probabilistic patch-based filtering, Dictionary learning filtering, K-SVD, Gaussian patch-PCA filtering, BM3D Review IntroductionThe noise level in digital images may vary from being almost imperceptible to being very noticeable. Image denoising techniques attempt to produce a new image that has less noise, i.e., closer to the original noise-free image. Image denoising techniques can be grouped into two main approaches: pixel-based image filtering and patch-based *Correspondence: elsakka@csd.uwo.ca 2 Department of Computer Science, Middlesex College, Western University, 1151 Richmond Street, N6A 5B7, London, Ontario, Canada Full list of author information is available at the end of the article image filtering. A pixel-based image filtering scheme is mainly a proximity operation used for manipulating one pixel at a time (pixel-wise) based on its spatial neighboring pixels located within a kernel. On the other hand, in patch-based image filtering, the noisy image is divided into patches, or "blocks, " which are then manipulated separately in order to provide an estimate of the true pixel values (patch-wise) based on similar patches located within a search window. This approach utilizes the redundancy and the similarity among the various parts of the input image. Figure 1 shows the mechanism of the two approaches.

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Section: Averaging Patch-based: Non-local Meansmentioning

confidence: 99%

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Alkinani

El-Sakka

2017

J Image Video Proc.

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“…A method similar to equation ( 9) was proposed inspired by Bowsher prior (Bowsher et al 2004) and it used w a (i, j|g) = 1 when g i /g j is in between pre-defined two values and w a (i, j|g) = 0 otherwise (Chan et al 2010). Similarly one can also design Bowsher prior-based NLM weights for post-reconstruction filtering as follows:…”

Section: Non-local Means Filtering With Side Informationmentioning

confidence: 99%

“…NLM filters also have been used as a regularizer in image reconstruction (Zhang et al 2010). Moreover, using these NLM methods with high-quality side information can improve image quality further (Deledalle et al 2010, Rousseau 2010, Chan et al 2010, Vunckx et al 2012, Nguyen and Lee 2012. Unlike other SPECT image reconstruction methods using CT side information (Fessler et al 1992, Gindi et al 1993, Dewaraja et al 2010a, these NLM-based methods using side information do not require CT image segmentation.…”

Section: Introductionmentioning

confidence: 99%

Post-reconstruction non-local means filtering methods using CT side information for quantitative SPECT

2013

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Quantitative SPECT techniques are important for many applications including internal emitter therapy dosimetry where accurate estimation of total target activity and activity distribution within targets are both potentially important for dose-response evaluations. We investigated non-local means (NLM) post-reconstruction filtering for accurate I-131 SPECT estimation of both total target activity and the 3D activity distribution. We first investigated activity estimation versus number of ordered-subsets expectation-maximization (OSEM) iterations. We performed simulations using the XCAT phantom with tumors containing a uniform and a non-uniform activity distribution, and measured the recovery coefficient (RC) and the root mean squared error (RMSE) to quantify total target activity and activity distribution, respectively. We observed that using more OSEM iterations is essential for accurate estimation of RC, but may or may not improve RMSE. We then investigated various post-reconstruction filtering methods to suppress noise at high iteration while preserving image details so that both RC and RMSE can be improved. Recently, NLM filtering methods have shown promising results for noise reduction. Moreover, NLM methods using high-quality side information can improve image quality further. We investigated several NLM methods with and without CT side information for I-131 SPECT imaging and compared them to conventional Gaussian filtering and to unfiltered methods. We studied four different ways of incorporating CT information in the NLM methods: two known (NLM CT-B and NLM CT-M) and two newly considered (NLM CT-S and NLM CT-H). We also evaluated the robustness of NLM filtering using CT information to erroneous CT. NLM CT-S and NLM CT-H yielded comparable RC values to unfiltered images while substantially reducing RMSE. NLM CT-S achieved −2.7 to 2.6% increase of RC compared to no filtering and NLM CT-H yielded up to 6% decrease in RC while other methods yielded lower RCs than them: Gaussian filtering (up to 11.8% decrease in RC), NLM method without CT (up to 9.5% decrease in RC), and NLM CT-M and NLM CT-B (up to 19.4% decrease in RC). NLM CT-S and NLM CT-H achieved 8.2 to 33.9% and −0.9 to 36% decreased RMSE on tumors compared to no filtering respectively while other methods yielded less reduced or increased RMSE: Gaussian filtering (up to 7.9% increase in RMSE), NLM method without CT (up to 18.3% increase in RMSE), and NLM CT-M and NLM CT-B (up to 31.5% increase in RMSE). NLM CT-S and NLM CT-H also yielded images with tumor shapes that better-matched the true shapes than other methods. All NLM methods using CT information were robust to small misregistration between SPECT and CT, but NLM CT-S and NLM CT-H were more sensitive than NLM CT-M and NLM CT-B to missing CT information.

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“…Two patches are viewed the same or called rotationally invariant, if they are the same up to rotation. While the patch space model and diffusion-based algorithms have been successfully applied to different fields, such as the inpainting problem [22][23][24][25] and the medical imaging problem [19,26,27], however, to the best of our knowledge, a companion theoretical study is lacking. It is not clear how we could correctly find the neighbors when noise exists, and why the patch size should be neither too big nor too small and thus why patch space approaches are better than pixel-based ones.…”

Section: Introductionmentioning

confidence: 99%

Manifold Learning via the Principle Bundle Approach

Lin

Minasian

et al. 2018

Front. Appl. Math. Stat.

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In this paper, we propose a novel principal bundle model and apply it to the image denoising problem. This model is based on the fact that the patch manifold admits canonical groups actions such as rotation. We introduce an image denoising algorithm, called the diffusive vector non-local Euclidean median (dvNLEM), by combining the traditional nonlocal Euclidean median (NLEM), the rotational structure in the patch space, and the diffusion distance. A theoretical analysis of dvNLEM, as well as the traditional nonlocal Euclidean median (NLEM), is provided to explain why these algorithms work. In particular, we show how accurate we could obtain the true neighbors associated with the rotationally invariant distance (RID) and Euclidean distance in the patch space when noise exists, and how we could apply the diffusion geometry to stabilize the selected metric. The dvNLEM is applied to an image database of 1,361 images and a comparison with the NLEM is provided. Different image quality assessments based on the error-sensitivity or the human visual system are applied to evaluate the performance.

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Median non-local means filtering for low SNR image denoising: Application to PET with anatomical knowledge

Cited by 25 publications

References 21 publications

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction

Post-reconstruction non-local means filtering methods using CT side information for quantitative SPECT

Manifold Learning via the Principle Bundle Approach

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