Composite Convolutional Neural Network for Noise Deduction

Xiu, Chunbo; Su, Xuemiao

doi:10.1109/access.2019.2936861

Cited by 14 publications

(6 citation statements)

References 42 publications

(37 reference statements)

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“…Convolution kernels are one of the most advantageous way to remove unnecessary data from the image and have been the subject of various noise reduction research, image recognition segmentation, edge detection, deep learning, 2456-8686, 5 (2), 2021:041-047 https://doi.org/10.26524/cm105 etc. The convolutions allow the whole image to be easily scanned its neighboring pixels [3]. The robustness to heavy deformation is another fascinating aspect and is something that regular kernels cannot do well due to their size [5].…”

Section: Introductionmentioning

confidence: 99%

Image extraction using convolution kernal

Sumathi¹,

Jothilakshmi²

2021

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This paper discusses about the noise reduction of images using the convolution matrix. The convolution kernel matrix filters generate new features of the input images with good quality.The noise reduction methods based on convolution kernel is achieved by deep learning theory along with the difference equations. The random variation of the colour and brightness are taken as authenticated coefficients of the images. Convolution techniques along withrecurrent neuralnetwork are applied into theinput image. This input image is divided into the matrix of pixel values. The optimal enhanced image is arrived through convolution kernel using computational learning of autonomous difference equations.

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Section: Introductionmentioning

confidence: 99%

Image extraction using convolution kernal

Sumathi¹,

Jothilakshmi²

2021

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“…This represents a major shortcoming of other studies. CNN has been demonstrated to constitute a powerful and suitable tool for image classification (Palm, 2012;Liang et al, 2018;Zhu et al, 2018;Lin et al, 2019b;Xiu et al, 2019;Xin et al, 2020). Therefore, the objective of this study is to detect TEC precursors of larger earthquakes by using a CNN (for convenience, the abbreviation "CNN" means two-dimensional CNN in this study) through the examination of TEC maps instead of TEC data to confirm TEC precursors of the Chi-Chi earthquake of September 21, 1999 and compare the results with those in the studies of Liu et al (2001) and Lin (2010).…”

Section: Introductionmentioning

confidence: 99%

Detecting Total Electron Content Precursors Before Earthquakes by Examining Total Electron Content Images Based on Butterworth Filter in Convolutional Neural Networks

Lin

Chiou

2020

IEEE Access

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Taiwan Standard Time [TST]) as training images (inputs) were used to create two convolutional neural network (CNN) models. However, splitting the TEC maps of the three time periods into daily TEC images caused wedge effects. The wedge effects were reduced using a low-pass filter called the Butterworth filter. This resulted in clearer TEC precursors for earthquakes, facilitating the identification of earthquakes of magnitude M w ≥ 5.0 that exhibited associated TEC precursors during three periods, particularly for the Chi-Chi earthquake of September 21, 1999. The results of this study were compared with those of Lin et al. (2001) and Lin (2010) associated with the Chi-Chi earthquake. Simultaneously, two CNN models that were developed were verified to be rational due to the high accuracy of their predictions. These two models were used to verify each other's accuracies and to demonstrate the reliability of the method in this study. Therefore, statistical analysis was not the aim. The final outputs of the two CNN model were defined as similarities. Similarities, which are larger than 0.5, were defined as TEC precursors of earthquakes. TEC precursors described as temporal TEC multi-precursors (TTMPs) by Zoran et al. (2018) were detectable on the 1st, 3rd, and 4th days (that is, on September 17, 18, and 20, 1999, respectively) prior to the Chi-Chi earthquake of September 21, 1999. These results are consistent with those of Liu et al. (2001) and Lin (2010). A TEC precursor on May 13, 2003, (TST) was detectable 2 days prior to the earthquake on May 15, 2003, (TST) with the magnitude (M w) of 5.52. The low standard deviation (STD) and mean square error (MSE) confirm the reliability of both CNN models. Regarding mechanical principles, the TTMPs related to the Chi-Chi earthquake were caused by an electric field (Liu et al., 2001). The cause of the TEC precursor on May 13, 2003, prior to the earthquake on May 15, 2003, was an argument without any corresponding study for comparison.

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“…The low-rank models, described in [ 22 , 23 , 24 ], are used for image restoration in order to deliver favorable results. Moreover, different methods based on deep learning have been explored for image denoising as explained in [ 25 , 26 , 27 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images

Awad

2020

J. Imaging

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In this paper, a new method for the removal of Gaussian noise based on two types of prior information is described. The first type of prior information is internal, based on the similarities between the pixels in the noisy image, and the other is external, based on the index or pixel location in the image. The proposed method focuses on leveraging these two types of prior information to obtain tangible results. To this end, very similar patches are collected from the noisy image. This is done by sorting the image pixels in ascending order and then placing them in consecutive rows in a new two-dimensional image. Henceforth, a principal component analysis is applied on the patch matrix to help remove the small noisy components. Since the restored pixels are similar or close in values to those in the clean image, it is preferable to arrange them using indices similar to those of the clean pixels. Simulation experiments show that outstanding results are achieved, compared to other known methods, either in terms of image visual quality or peak signal to noise ratio. Specifically, once the proper indices are used, the proposed method achieves PSNR value better than the other well-known methods by >1.5 dB in all the simulation experiments.

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Composite Convolutional Neural Network for Noise Deduction

Cited by 14 publications

References 42 publications

Image extraction using convolution kernal

Image extraction using convolution kernal

Detecting Total Electron Content Precursors Before Earthquakes by Examining Total Electron Content Images Based on Butterworth Filter in Convolutional Neural Networks

Fusion of External and Internal Prior Information for the Removal of Gaussian Noise in Images

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