Structure preserving image denoising based on low-rank reconstruction and gradient histograms

Zhang, Mingli; Desrosiers, Christian

doi:10.1016/j.cviu.2018.05.006

Cited by 21 publications

(3 citation statements)

References 35 publications

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“…Finally, the collaborative filtering for the aggregation of the smoothed patches is performed as in the 3D block-matching method. The weighted nuclear norm and the histogram preservation (Zhang & Desrosiers 2018) are combined in a single constrained optimisation problem, that is solved through the alternating direction method of multipliers (Boyd et al 2011).…”

Section: Anisotropic Methodsmentioning

confidence: 99%

A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images

Cammarasana¹,

Nicolardi²,

Patanè³

2021

Preprint

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Ultrasound images are widespread in medical diagnosis for muscleskeletal, cardiac, and obstetrical diseases, due to the efficiency and noninvasiveness of the acquisition methodology. However, ultrasound acquisition introduces a speckle noise in the signal, that corrupts the resulting image and affects further processing operations, and the visual analysis that medical experts conduct to estimate patient diseases. Our main goal is to define a universal deep learning framework for real-time denoising of ultrasound images. We analyse and compare state-of-the-art methods for the smoothing of ultrasound images (e.g., spectral, low-rank, and deep learning denoising algorithms), in order to select the best one in terms of accuracy, preservation of anatomical features, and computational cost. Then, we propose a tuned version of the selected state-of-the-art denoising methods (e.g., WNNM), to improve the quality of the denoised images, and extend its applicability to ultrasound images. To handle large data sets of ultrasound images with respect to applications and industrial requirements, we introduce a denoising framework that exploits deep learning and HPC tools, and allows us to replicate the results of state-of-the-art denoising methods in a real-time execution.

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Section: Anisotropic Methodsmentioning

confidence: 99%

A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images

Cammarasana¹,

Nicolardi²,

Patanè³

2021

Preprint

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“…The collaborative filtering of WNNM for the aggregation of the denoised patches is performed as in the 3D block-matching method. The weighted nuclear norm and the histogram preservation [ 74 ] are combined in a single constrained optimisation problem, which is solved through the alternating direction method of multipliers [ 10 ]. The WNNM is extended to image deblurring with several types of noise [ 40 ].…”

Section: Related Workmentioning

confidence: 99%

Real-time denoising of ultrasound images based on deep learning

Cammarasana

Nicolardi

Patanè

2022

Med Biol Eng Comput

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Ultrasound images are widespread in medical diagnosis for muscle-skeletal, cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness of the acquisition methodology. However, ultrasound acquisition introduces noise in the signal, which corrupts the resulting image and affects further processing steps, e.g. segmentation and quantitative analysis. We define a novel deep learning framework for the real-time denoising of ultrasound images. Firstly, we compare state-of-the-art methods for denoising (e.g. spectral, low-rank methods) and select WNNM (Weighted Nuclear Norm Minimisation) as the best denoising in terms of accuracy, preservation of anatomical features, and edge enhancement. Then, we propose a tuned version of WNNM (tuned-WNNM) that improves the quality of the denoised images and extends its applicability to ultrasound images. Through a deep learning framework, the tuned-WNNM qualitatively and quantitatively replicates WNNM results in real-time. Finally, our approach is general in terms of its building blocks and parameters of the deep learning and high-performance computing framework; in fact, we can select different denoising algorithms and deep learning architectures.

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“…Some research has introduced TV regulation into low-rank models [17][18][19][20], trying to take advantage of the edge-preserving ability of the TV algorithm. A new method that makes use of low-rank regularization and a texture preserving prior [21] has then been proposed. Meanwhile, LRTL0 [22] uses the l 0 gradient regularization to realize edge preservation.…”

Section: Introductionmentioning

confidence: 99%

Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

et al. 2023

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The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many important details, especially the edges. In this paper, we propose a new denoising method named EPLRR-RSID, which focuses on edge preservation to improve the image quality of the details. Specifically, we considered the low-rank residues as a combination of useful edges and noisy components. In order to better learn the edge information from the low-rank representation (LRR), we designed multi-level knowledge to further distinguish the edge part and the noise part from the residues. Furthermore, a manifold learning framework was introduced in our proposed model to better obtain the edge information, as it can find the structural similarity of the edge part while suppressing the influence of the non-structural noise part. In this way, not only the low-rank part is better learned, but also the edge part is precisely preserved. Extensive experiments on synthetic and several real remote sensing datasets showed that EPLRR-RSID has superior advantages over the compared state-of-the-art (SOTA) approaches, with the mean edge protect index (MEPI) values reaching at least 0.9 and the best values in the no-reference index BRISQUE, which represents that our method improved the image quality by edge preserving.

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Structure preserving image denoising based on low-rank reconstruction and gradient histograms

Cited by 21 publications

References 35 publications

A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images

A Universal Deep Learning Framework for Real-Time Denoising of Ultrasound Images

Real-time denoising of ultrasound images based on deep learning

Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

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