Iterative weighted sparse representation for X‐ray cardiovascular angiogram image denoising over learned dictionary

Huang, Zhenghua; Li, Qian; Zhang, Tianxu; Sang, Nong; Hu, Hong

doi:10.1049/iet-ipr.2017.0518

Cited by 70 publications

(27 citation statements)

References 34 publications

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“…To achieve excellent assessments of the sparsely coding coefficients of the residual image and to make images denoise while maintaining their textures, a robust algorithm mixing gradient histogram with SR is used [12]. Completive denoising efficiency and high quality images can be achieved in comparison to other denoising techniques by an iterative weighted sparse representation (IWSR) [13]. The objective of a dual domain filter (DDF) is to enhance the SAGD image quality [14].…”

Section: Introductionmentioning

confidence: 99%

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A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

Elsayed¹,

Salem²,

Aly³

2020

IJIES

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Noise can affect images while acquired, transmitted, stored or compressed. One of the best methods for noise removal is the sparse representation algorithm (SR). The Quantum Particle Swarm Optimization (QPSO) is one of the meta-heuristic algorithms. This paper shows excellent results in noise reduction in the quick version of QPSO, which uses benefit of the SRs and meta-heuristic algorithms. This approach is known as FQPSO-MP, depending on the matching pursuit algorithm (MP). A proposed Dynamic-Multi-Swarm (DMS) and a pre-learned dictionary (FQPSO-MP) method saves the time of calculating the learning dictionary. These modifications contribute to important benefits of computing efficiency (productivity improvements of approximately 90% are achieved) without sacred image quality in comparison with the initial QPSO-MP technique (the bigger reduction relative to the PSNR indexes is lower than 0.58 dB and 0.019). The proposed FQPSO-MP method compared to the original QPSO-MP method after modification. The scientific results show that the FQPSO-MP algorithm is more effective and quicker without sacrificing image quality than the FQPSO-MP algorithm. The experimental results show, in comparison to state-of-the-art denoising algorithms, that both quantitative and image quality results are achieved with the suggested FQPSO-MP method.

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Section: Introductionmentioning

confidence: 99%

“…International Journal of Intelligent Engineering and Systems, Vol 13,. No.1, 2020 DOI: 10.22266/ijies2020.0229.10Table 3.…”

mentioning

confidence: 99%

A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

Elsayed¹,

Salem²,

Aly³

2020

IJIES

View full text Add to dashboard Cite

show abstract

“…Statistical estimators of all sorts, spatial adaptive filters, transform‐domain methods, splines [1–3], stochastic analysis, total variation (TV) methods [4, 5], and other approximation theory methods, morphological analysis, order statistics, and more are some of the many directions explored in studying this problem. Sparse and redundant representations based methods [6, 7], as well as non‐negative matrix factorisation technique [8], are very important progress in image denoising problem in the past decade and have been widely used [9–13]. Non‐local means (NLM) [14, 15] and block matching 3D (BM3D) algorithm [16, 17] use non‐local self‐similarity to separate image and noise.…”

Section: Introductionmentioning

confidence: 99%

Image DAEs based on residual entropy maximum

2020

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Image denoising under low signal‐to‐noise ratio (SNR) and non‐Gaussian noise is still a challenging problem in image processing. In this study, the authors prose a kind of improved convolution neural network auto‐encoders for image denoising. Different from other priors based methods, the denoising auto‐encoders (DAEs) can learn end‐to‐end mappings from noisy images to the target ones. This study research statistical features of image residual between the restored images and target images. According to the maximum entropy principle, the training loss function of the ordinary DAEs was modified with residual statistics as the constraint condition, and an improved training algorithm was proposed based on augmented Lagrange function method. Thus, the quality of restored image can be improved through removing image information from residual more efficiently. Experiments show not only the denoising effects of improved DAEs is superior to the original mean‐square‐error loss function DAEs in both peak SNR and Riesz feature similarity metric indexes, but also has the ability to suppress the different types of noises with different levels through a single model.

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“…However, the method of disease detection based on human perception is error-prone and unsophisticated, especially for extensive EMG data. Considering the problem as stated above, researchers over the last few decades are trying to develop a more accurate and fast disease detection system using signal processing and machine learning algorithms for reliable detection and diagnosis of different physiological ailments [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders

Chatterjee

Roy

Bose³

et al. 2020

IET Science, Measurement & Technology

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Iterative weighted sparse representation for X‐ray cardiovascular angiogram image denoising over learned dictionary

Cited by 70 publications

References 34 publications

A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

Image DAEs based on residual entropy maximum

Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders

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