Attention-guided CNN for image denoising

Tian, Chunwei; Xu, Yong; Li, Zuoyong; Zuo, Wangmeng; Zhang, Bob; Liu, Hong

doi:10.1016/j.neunet.2019.12.024

Cited by 467 publications

(241 citation statements)

References 39 publications

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“…On the one hand, the neural network can learn the attention mechanism autonomously; on the other hand, the attention mechanism can in turn help us understand the world presented by neural network. In recent years, most of the research on the combining of deep learning and visual attention [16][17][18] focuses on using masks to form the attention mechanism. The principle of using the mask as the attention mechanism lies in the extraction of key features from the image using the weights predicted by the neural network.…”

Section: Methodsmentioning

confidence: 99%

An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

et al. 2020

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Pneumonia is a disease that develops rapidly and seriously threatens the survival and health of human beings. At present, the computer-aided diagnosis (CAD) of pneumonia is mostly based on binary classification algorithms that cannot provide doctors with location information. To solve this problem, this study proposes an end-to-end highly efficient algorithm for the detection of pneumonia based on a convolutional neural network—Pneumonia Yolo (PYolo). This algorithm is an improved version of the Yolov3 algorithm for X-ray image data of the lungs. Dilated convolution and an attention mechanism are used to improve the detection results of pneumonia lesions. In addition, double K-means is used to generate an anchor box to improve the localization accuracy. The algorithm obtained 46.84 mean average precision (mAP) on the X-ray image dataset provided by the Radiological Society of North America (RSNA), surpassing other detection algorithms. Thus, this study proposes an improved algorithm that can provide doctors with location information on lesions for the detection of pneumonia.

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Section: Methodsmentioning

confidence: 99%

An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

et al. 2020

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“…Inspired by ADNet (Tian et al, 2020), the FFA-DMRI is proposed to eliminate noise in MRI. Figure 1 illustrates the overall architecture of FFA-DMRI.…”

Section: Proposed Methodsmentioning

confidence: 99%

FFA-DMRI: A Network Based on Feature Fusion and Attention Mechanism for Brain MRI Denoising

et al. 2020

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Magnetic Resonance Imaging (MRI) is an indispensable tool in the diagnosis of brain diseases due to painlessness and safety. Nevertheless, Rician noise is inevitably injected during the image acquisition process, which leads to poor observation and interferes with the treatment. Owing to the complexity of Rician noise, using the elimination method of Gaussian to remove it does not perform well. Therefore, the feature fusion and attention network (FFA-DMRI) is proposed to separate noise from observed MRI. Inspired by the attention-guided CNN network (ADNet) and Convolutional block attention module (CBAM), a spatial attention mechanism has been specially designed to obtain the area of interest in MRI. Furthermore, the feature fusion block concatenates local with global information, which makes full use of the multilevel structure and boosts the expressive ability of network. The comprehensive experiments on Alzheimer's disease neuroimaging initiative dataset (ADNI) have demonstrated high effectiveness of FFA-DMRI with maintaining the crucial brain details. Moreover, in terms of visual inspections, the denoising results are also consistent with human perception.

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“…This may cause big risk in real-world applications, since everyone can pass the system by a black image. Many deep learning-based image denoising methods have been proposed [54,55], but, to some extent, they are too time-consuming to the target of palmprint image preprocessing. Hence, a high-speed abnormal ROI detection method is required.…”

Section: Abnormal Detection and Iterative Localizationmentioning

confidence: 99%

Region of Interest Localization Methods for Publicly Available Palmprint Databases

Xu¹,

Fan²,

Li³

et al. 2021

Biometric Systems

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So far, there exist many publicly available palmprint databases. However, not all of them have provided the corresponding region of interest (ROI) images. If everyone uses their own extracted ROI images for performance testing, the final accuracy is not strictly comparable. Since ROI localization is the critical stage of palmprint recognition. The location precision has a significant impact on the final recognition accuracy, especially in unconstrained scenarios. This problem has limited the applications of palmprint recognition. However, many currently published surveys only focus on feature extraction and classification methods. Throughout these years, many new ROI localization methods have been proposed. In this chapter, we will group the existing ROI localization methods into different categories, analyze their basic ideas, reproduce some of the codes, make comparisons of their performances, and provide further directions. We hope this could be a useful reference for further research.

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Attention-guided CNN for image denoising

Cited by 467 publications

References 39 publications

An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

An Improved Algorithm for Detecting Pneumonia Based on YOLOv3

FFA-DMRI: A Network Based on Feature Fusion and Attention Mechanism for Brain MRI Denoising

Region of Interest Localization Methods for Publicly Available Palmprint Databases

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