A Novel Approach Using Convolutional Neural Network to Reconstruct Image resolution

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

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A novel approach to reconstruction based saliency detection via convolutional neural network stacked with auto-encoder

Neurocomputing ◽

10.1016/j.neucom.2019.01.041 ◽

2019 ◽

Vol 349 ◽

pp. 145-155 ◽

Cited By ~ 7

Author(s):

Xinchen Lin ◽

Yang Tang ◽

Huaglory Tianfield ◽

Feng Qian ◽

Weimin Zhong

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Saliency Detection ◽

Novel Approach

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Estimation of Ultrasound Echogenicity Map from B-Mode Images Using Convolutional Neural Network

Sensors ◽

10.3390/s20174931 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4931

Author(s):

Che-Chou Shen ◽

Jui-En Yang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Imaging System ◽

Speckle Pattern ◽

Image Resolution ◽

Post Processing ◽

Point Spread ◽

Spread Function ◽

In Silico Simulation ◽

Tissue Structures

In ultrasound B-mode imaging, speckle noises decrease the accuracy of estimation of tissue echogenicity of imaged targets from the amplitude of the echo signals. In addition, since the granular size of the speckle pattern is affected by the point spread function (PSF) of the imaging system, the resolution of B-mode image remains limited, and the boundaries of tissue structures often become blurred. This study proposed a convolutional neural network (CNN) to remove speckle noises together with improvement of image spatial resolution to reconstruct ultrasound tissue echogenicity map. The CNN model is trained using in silico simulation dataset and tested with experimentally acquired images. Results indicate that the proposed CNN method can effectively eliminate the speckle noises in the background of the B-mode images while retaining the contours and edges of the tissue structures. The contrast and the contrast-to-noise ratio of the reconstructed echogenicity map increased from 0.22/2.72 to 0.33/44.14, and the lateral and axial resolutions also improved from 5.9/2.4 to 2.9/2.0, respectively. Compared with other post-processing filtering methods, the proposed CNN method provides better approximation to the original tissue echogenicity by completely removing speckle noises and improving the image resolution together with the capability for real-time implementation.

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