Data Adaptive Dual Domain Denoising: a Method to

Pierazzo, Nicola; Facciolo, Gabriele

doi:10.5201/ipol.2017.203

Cited by 6 publications

(6 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our Oracle scenario showed that HOSVD might be a good opponent to state-of-the-art algorithms when correctly tuned and with an efficient second pass. We were not able to provide really good increases in PSNR with the use of the DA3D algorithm [14] and with SOS Boosting [17], contrarily to what was expected from the experiments provided in the reference paper. For the latter, we did not observe any positive influence for HOSVD probably due to an excess of smoothing during the first passes of SOS Boosting.…”

Section: Resultscontrasting

confidence: 64%

“…In this section, we introduce several extensions of the implementation of the presented algorithm [16]. We propose to perform iterative denoising with the one step enhancement introduced in DA3D [14] and the iterative SOS Boosting procedure [17]. These extensions are followed by an Oracle scenario given in [16] for the SVD denoising and applied here to the HOSVD decomposition.…”

Section: Extensionsmentioning

confidence: 99%

“…We tested the use of the DA3D method [14] as a black-box to compute the influence of one step of iteration on the result of a denoising algorithm. We used Wiener filtering of HOSVD as the input of the DA3D algorithm.…”

Section: A Step Furthermentioning

confidence: 99%

“…Having obtained a first estimation of the clean image I clean , a final estimation is computed using either Wiener filtering for BM3D or collaborative filtering for NL-Bayes. This idea of iterative denoising is not new and also leads to methods like SOS Boosting [17] or DDID [7] and DA3D [14]. One of the drawbacks of iterative denoising is the tendency to oversmooth the denoised image due to the regularity principle used in the iterations.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Implementation of a Denoising Algorithm Based on High-Order Singular Value Decomposition of Tensors

Feschet¹

2019

Image Processing on Line

View full text Add to dashboard Cite

This article presents an implementation of a denoising algorithm based on High-Order Singular Value Decomposition (HOSVD) of tensors. It belongs to the class of patch-based methods such as BM3D and NL-Bayes. It exploits the grouping of similar patches in a local neighborhood into a 3D matrix also called a third order tensor. Instead of performing a different processing in each dimension, as in BM3D for instance, it is based on the decomposition of a tensor simultaneously in all dimensions reducing it to a core tensor in a similar way as SVD does for matrices in computing the diagonal matrix of singular values. The core tensor is filtered and a tensor is reconstructed by inverting the HOSVD. As it is common in patch-based algorithms, all tensors containing a pixel are then merged to produce an output image. Source Code The C++ source code, the code documentation, and the online demo are accessible at the IPOL web page of this article. 1 Compilation and usage instructions are included in the README.txt file of the archive.

show abstract

Section: Resultscontrasting

confidence: 64%

Section: Extensionsmentioning

confidence: 99%

Section: A Step Furthermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Implementation of a Denoising Algorithm Based on High-Order Singular Value Decomposition of Tensors

Feschet¹

2019

Image Processing on Line

View full text Add to dashboard Cite

show abstract

“…Frequency domain techniques, as opposed to spatial domain methods, excel in preserving low-contrast details and textures while suffering from inaccurate edge denoising where some Gibbs ringing artefacts can be observed near strong edges (Le Pogam et al 2013, Boashash 2015. The idea here is to take advantage of both spatial and transfer domain filtering to accomplish artefact free denoising while preserving low-contrast features as was explored earlier on natural images (Knaus and Zwicker 2013, Pierazzo et al 2014, Pierazzo and Facciolo 2017. For the spatial domain denoising, we chose non-local mean (NLM) approach, which reduces noise by calculating the weighted average of voxels based on similarities between two patches of voxels.…”

Section: Introductionmentioning

confidence: 99%

Improvement of image quality in PET using post-reconstruction hybrid spatial-frequency domain filtering

Arabi

Zaidi

2018

Phys. Med. Biol.

View full text Add to dashboard Cite

PET images commonly suffer from the high noise level and poor signal-to-noise ratio (SNR), thus adversely impacting lesion detectability and quantitative accuracy. In this work, a novel hybrid dualdomain PET denoising approach is proposed, which combines the advantages of both spatial and transform domain filtering to preserve image textures while minimizing quantification uncertainty. Spatial domain denoising techniques excel at preserving high-contrast patterns compared to transform domain filters, which perform well in recovering low-contrast details normally smoothed out by spatial domain filters. For spatial domain filtering, the non-local mean algorithm was chosen owing to its performance in denoising high-contrast features whereas multi-scale curvelet denoising was exploited for the transform domain owing to its capability to recover small details. The proposed hybrid method was compared to conventional post-reconstruction Gaussian and edge preserving bilateral filters. Computer simulations of a thorax phantom containing three small lesions, experimental measurements using the Jaszczak phantom and clinical whole-body PET/ CT studies were used to evaluate the performance of the proposed PET denoising technique. The proposed hybrid filter increased the SNR from 8.0 (non-filtered PET image) to 39.3 for small lesions in the computerized thorax phantom, while Gaussian and bilateral filtering led to SNRs of 23.3 and 24.4, respectively. For the experimental Jaszczak phantom, the contrast-to-noise ratio (CNR) improved from 10.84 when using Gaussian smoothing to 14.02 and 19.39 using the bilateral and the proposed hybrid filters, respectively. The clinical studies further demonstrated the superior performance of the hybrid method, yielding a quantification change (the original noisy OSEM image was used as reference in the absence of ground truth) in malignant lesions of −2.4% compared to −11.9% and −6.6% achieved using Gaussian and bilateral filters, respectively. In some cases, the visual difference between the bilateral and hybrid filtered images is not substantial; however the improved CNR score from 11.3 by OSEM to 17.1 and 21.8 by bilateral to the hybrid filtering, respectively, demonstrates the overall gain achieved by the hybrid approach. The proposed hybrid algorithm improved the contrast, SNR and quantitative accuracy compared to Gaussian and bilateral approaches, and can be utilized as an alternative post-reconstruction filter in clinical PET/CT imaging.

show abstract

Spatially guided nonlocal mean approach for denoising of PET images

Arabi

Zaidi

2020

Medical Physics

View full text Add to dashboard Cite

Purpose: Nonlocal mean (NLM) filtering proved to be an effective tool for noise reduction in natural and medical imaging. The technique relies on existing redundant information in the input image to discriminate the genuine signal from noise. However, due to the prohibitively long computation time, the search for finding similar information is confined by a predefined search window, which may hamper the performance of this filter. In this work, a spatially guided non local mean (SG-NLM) approach was proposed to overcome this issue. The proposed method was evaluated on whole-body positron emission tomography images presenting with high noise levels, which adversely affect lesion detectability and quantitative accuracy. Methods: In the SG-NLM method, as opposed to the conventional NLM method, where a predefined search window is defined to confine exhaustive search for finding similar patterns, the information about similar patterns is extracted from the clustered version (created based on signal intensity levels) of the input image as well as information about prominent edges. The performance of the SG-NLM was evaluated against post-reconstruction NLM, Gaussian, bilateral and BayesShrink Wavelet denoising approaches. A digital phantom containing three small inserts mimicking lesions in the lung, experimental study using the Jaszczak phantom and whole-body PET/CT clinical studies were utilized to assess the performance of abovementioned denoising approaches. Results: The SG-NLM method led to a signal-to-noise (SNR) increase from 21.3 (unfiltered PET image) to 30.1 in computer simulations of small lesions while the NLM mean filer resulted in an SNR of 29.4 (P < 0.05). The experimental Jaszczak phantom study demonstrated that the contrastto-noise ratio (CNR) increased from 11.3 when using the Gaussian filter to 18.6 and 19.5 when using NLM and SG-NLM filters (P < 0.05), respectively. The superior performance of the SG-NLM approach was confirmed by clinical studies where the bias in malignant lesions decreased to À2.3 AE 1.1% compared to À11.7 AE 2.4 and À2.9 AE 1.1 achieved using the Gaussian and NLM methods (P < 0.05), respectively. Conclusions: The proposed SG-NLM achieves promising compromise between noise reduction and signal preservation compared to the conventional NLM method. The superior performance of the SG-NLM method was accomplished without adding extra burden to the computational complexity of the conventional NLM filter, which makes it attractive for denoising PET images.

show abstract

Data Adaptive Dual Domain Denoising: a Method to

Cited by 6 publications

References 37 publications

Implementation of a Denoising Algorithm Based on High-Order Singular Value Decomposition of Tensors

Implementation of a Denoising Algorithm Based on High-Order Singular Value Decomposition of Tensors

Improvement of image quality in PET using post-reconstruction hybrid spatial-frequency domain filtering

Spatially guided nonlocal mean approach for denoising of PET images

Contact Info

Product

Resources

About