Wavelet-Based Self-adaptive Hierarchical Thresholding Algorithm and Its Application in Image Denoising

Zhang, Jianhua; Zhu, Qiaoming; Lin, Shi‐Ming

doi:10.18280/ts.360609

Cited by 5 publications

(4 citation statements)

References 23 publications

(24 reference statements)

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“…In 2019, Zhao [11] bstudied an improved SFA algorithm based on visual invariance, and applied it to extract features of natural images, which achieved good results. The study confirmed the invariance of slow features and slow structures in the visual space, and the proposed algorithm proved to have good anti-noise capacity [12]. In 2020, Zhao [13] studied an improved SFA algorithm based on visual selectivity, and applied it in the feature extraction of limited defocused image sequences, which achieved good results.…”

Section: Introductionmentioning

confidence: 70%

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Yang¹,

Zhao²,

Su³

et al. 2021

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The conventional slow feature analysis (SFA) algorithm has no support of computational theory of vision for primates, nor does it have the ability to learn the global features with visual selection consistency continuity. And what is more, the algorithm is highly complex. Based on this, Slow Feature Extraction Algorithm Based on Visual selection consistency continuity and Its Application was proposed. Inspired by the visual selection consistency continuity theory for primates, this paper replaced the principal component analysis (PCA) method of the conventional SFA algorithm with the myTICA method, extracted the Gabor basis functions of natural images, initialized the basis function family; it used the feature basis expansion algorithm based on visual selection consistency continuity (the VSCC_FBEA algorithm) to replace the polynomial expansion method in the original SFA algorithm to generates the Gabor basis functions of features with long and short-term visual selectivity in the family of basis functions, which solved the drawbacks of the polynomial prediction algorithm; it also designed the Lipschitz consistency constraint, and proposed the Lipschitz-Orthogonal-Pruning-Method (LOPM algorithm) to optimize the basis function family into an over-complete family of basis functions. In addition, this paper used the feature expression method based on visual invariance theory (visual invariance theory -FEM) to establish the set of features of natural images with visual selection consistency continuity. Subsequently, it adopted three error evaluation methods and mySFA classification method to evaluate the proposed algorithm. According to the experimental results, the proposed algorithm showed good prediction performance with respect to the LSVRC2012 data set; compared with the SFA, GSFA, TICA, myICA and mySFA algorithms, the proposed algorithm is correct and feasible; when the classification threshold of the algorithm was set at 8.0, the recognition rate of the proposed algorithm reached 99.66%, and neither of the false recognition rate and the false rejection rate was higher than 0.33%. The proposed algorithm has good performance in prediction and classification, and also shows good anti-noise capacity under limited noise conditions.

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Section: Introductionmentioning

confidence: 70%

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Yang¹,

Zhao²,

Su³

et al. 2021

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“…Feature extraction is one of the important pre-processing steps in the classification process. Wavelet transform [13,14], ridgelet transform [15], ripplet transform [16], histogram of the gradient [17,18], and local binary pattern (LBP) are some of the many known feature extraction methods. By using these methods, edges, orientation, volume, resolution, and histogram are obtained from the images [19,20].…”

Section: Feature Extractionmentioning

confidence: 99%

Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms

Örnek¹,

Ervural²,

Ceylan³

et al. 2020

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Monitoring and evaluating the skin temperature value are considerably important for neonates. A system detecting diseases without any harmful radiation in early stages could be developed thanks to thermography. This study is aimed at detecting healthy/unhealthy neonates in neonatal intensive care unit (NICU). We used 40 different thermograms belonging 20 healthy and 20 unhealthy neonates. Thermograms were exported to thermal maps, and subsequently, the thermal maps were converted to a segmented thermal map. Local binary pattern and fast correlation-based filter (FCBF) were applied to extract salient features from thermal maps and to select significant features, respectively. Finally, the obtained features are classified as healthy and unhealthy with decision tree, artificial neural networks (ANN), logistic regression, and random forest algorithms. The best result was obtained as 92.5% accuracy (100% sensitivity and 85% specificity). This study proposes fast and reliable intelligent system for the detection of healthy/unhealthy neonates in NICU.

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“…The traditional image denoising methods mainly include mean filtering [9], median filtering [10] and wavelet transform [11], which are insufficient because they have poor robustness in the real world and cannot accurately and efficiently remove noise from images [12]. With the development of deep learning, image denoising algorithms based on deep learning have been widely used due to their efficient and convenient characteristics and have become an effective solution to deal with image denoising.…”

Section: Introductionmentioning

confidence: 99%

“…where GELU belongs to the class of activation functions, and We use the feed-forward module (FFM) to enhance the nonlinear expression of FFCA. The computational procedure is shown in Equation (11):…”

Section: Feed Forward Network Introductionmentioning

confidence: 99%

CA-BSN: Mural Image Denoising Based on Cross–Attention Blind Spot Network

Cai,

Liu,

Liu

et al. 2024

Applied Sciences

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Recently, Asymmetric pixel–shuffle downsampling and Blind–Spot Network (AP-BSN) has made some progress in unsupervised image denoising. However, the method tends to damage the texture and edge information of the image when using pixel-shuffle downsampling (PD) to destroy pixel-related large-scale noise. To tackle this issue, we suggest a denoising method for mural images based on Cross Attention and Blind–Spot Network (CA-BSN). First, the input image is downsampled using PD, and after passing through a masked convolution module (MCM), the features are extracted respectively; then, a cross attention network (CAN) is constructed to fuse the extracted feature; finally, a feed-forward network (FFN) is introduced to strengthen the correlation between the feature, and the denoised processed image is output. The experimental results indicate that our proposed CA-BSN algorithm achieves a PSNR growth of 0.95 dB and 0.15 dB on the SIDD and DND datasets, respectively, compared to the AP-BSN algorithm. Furthermore, our method demonstrates a SSIM growth of 0.7% and 0.2% on the SIDD and DND datasets, respectively. The experiments show that our algorithm preserves the texture and edge details of the mural images better than AP-BSN, while also ensuring the denoising effect.

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Wavelet-Based Self-adaptive Hierarchical Thresholding Algorithm and Its Application in Image Denoising

Cited by 5 publications

References 23 publications

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Classification of Medical Thermograms Belonging Neonates by Using Segmentation, Feature Engineering and Machine Learning Algorithms

CA-BSN: Mural Image Denoising Based on Cross–Attention Blind Spot Network

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