A novel image edge smoothing method based on convolutional neural network

Xu, Hui-hong; Ge, Dongyuan

doi:10.1177/1729881420921676

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…Hui-hong Xu et al have proposed to resolve the problem of edge detection and information capture better, significantly improves the edge effect and defend the efficiency of edge information. It reduces the signal-to-noise ratio of the smoothed image and greatly develops the effect of image smoothing [1]. Zhen Zheng et al haveproposed a novel image edge detection algorithm based on the gray prediction model.…”

Section: Review Of Literaturementioning

confidence: 99%

Performance analysis of gradient based image edge detection

Kasthuri

2022

ijhs

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Edge detection is a dynamic area of research and it gives support to higher level image analysis. Edge detection is a method of image processing used to make points in a digital image with discontinuities, sharp modifiers in the image brightness. Edge detection methods used in this work are Sobel, Prewitt, Roberts, Laplacian of Gaussian and Canny. The performance evaluation measures such as accuracy and specificity calculated from the Berkeley Segmentation dataset. Edge detection is used for object detection which offers various applications like medical field, industrial field, biometrics etc.

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Section: Review Of Literaturementioning

confidence: 99%

Performance analysis of gradient based image edge detection

Kasthuri

2022

ijhs

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“…Advanced DL techniques, including convolutional long shortterm memory [35], convolutional neural networks (CNNs) [36,37], generative adversarial networks (GANs), and conditional GAN (CGAN) [38][39][40][41][42][43][44], have been utilized in a number of satellite remote sensing researches [45][46][47][48]. These techniques have exhibited beneficial results in overcoming the limitations of conventional approaches relying on satellite observations.…”

Section: Introductionmentioning

confidence: 99%

Generation of Hypothetical Radiances for Missing Green and Red Bands in Geostationary Environment Monitoring Spectrometer

Ryu

Park

Jeong

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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True-color imagery is essential for an intuitive comprehension of atmospheric data. However, the Geostationary Environment Monitoring Spectrometer (GEMS) of the Geostationary Korea Multi-Purpose Satellite (GK)-2B lacks green and red bands, which limits its ability to monitor atmospheric environments. To mitigate this issue, we suggest an innovative method of generating virtual GEMS green and red bands using conditional generative adversarial networks with data observed in the blue-green-red (RGB) bands of the Advanced Meteorological Imager (AMI) sensor, a payload of the GK-2A satellite. The paired datasets of the AMI blue band and the AMI RGB bands were used to train and test the data-to-data (D2D) translation model. Using the GEMS blue band as input data, the D2D model generated GEMS hypothetical radiance data at the green and red bands. Our results show that the D2D model generated hypothetical GEMS green and red bands with outstanding performance. The averaged values of the correlation coefficient, root-mean-square error, and bias between the observed and D2D-generated GEMS blue band were 0.999, 3.450 W/cm²/cm/sr, and -1.858 W/cm²/cm/sr, respectively. This research is expected to significantly contribute to the monitoring and comprehension of atmospheric environments in Asia and potentially improve the GEMS's global ability to monitor air quality. Additionally, the proposed method has the potential to enhance the capabilities of other satellites with limited spectral bands.

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“…Numerous studies utilizing satellite data have demonstrated the effectiveness of the ML and DL methods [19]. The state-of-theart DL techniques, such as convolutional neural networks (CNN) [20,21], convolutional long short-term memory [22], and conditional generative adversarial networks (cGAN) [23][24][25][26][27][28][29], have been used in various satellite remote sensing studies [30][31][32][33]. DL techniques have shown to overcome the limitations of traditional approaches of satellite observations.…”

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confidence: 99%

Hypothetical Visible Bands of Advanced Meteorological Imager Onboard the Geostationary Korea Multi-Purpose Satellite -2A Using Data-To-Data Translation

Han

Jang

Ryu

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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True-color imagery of satellites provides various atmospheric and surface information for intuitive understanding and visualization. This study presents a conditional generative adversarial network method for generating daytime and nighttime hypothetical visible (VIS) bands of the Advanced Meteorological Imager (AMI) sensor onboard the Geostationary Korea Multi-Purpose Satellite. The AMI datasets in the form of albedo and brightness temperature (𝑻 𝑩 ) were normalized and denormalized between 0 and 1 data-to-data (D2D) translation. The D2D model was trained and tested using data pair of the albedos at AMI VIS bands and AMI infrared (IR) bands or 𝑻 𝑩 differences between two AMI IR bands. The constructed D2D model showed that the statistical results of bias, root-mean-square-error, and correlation coefficient between the observed and D2D-generated AMI VIS bands during daytime were -0.006 and 0.047 in albedo, and 0.941 for the blue band; -0.007 and 0.05 in albedo and 0.939 for the green band; -0.01 and 0.061 in albedo, and 0.917 for the red band, respectively. The proposed D2D method is being officially used by the Korea Meteorological Administration. Except for simulating desert areas as clouds at night, the D2D model demonstrated excellent performance, generating hypothetical AMI VIS bands at day and night. Consequently, this study could significantly contribute to the monitoring and understanding of meteorological phenomena over one-third of the Earth. Additionally, the method can be extended to other geostationary weather satellites, including Himawari-8, Fengyun-4A, Meteosat Third Generation, and Geostationary Operational Environmental Satellites.

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A novel image edge smoothing method based on convolutional neural network

Cited by 7 publications

References 15 publications

Performance analysis of gradient based image edge detection

Performance analysis of gradient based image edge detection

Generation of Hypothetical Radiances for Missing Green and Red Bands in Geostationary Environment Monitoring Spectrometer

Hypothetical Visible Bands of Advanced Meteorological Imager Onboard the Geostationary Korea Multi-Purpose Satellite -2A Using Data-To-Data Translation

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