scholarly journals Gaussian Mixture Model and Lifting Wavelet Transformed Base Satellite Image Enhancement

2019 ◽  
Vol 8 (2) ◽  
pp. 29-34
Keyword(s):  
Wavelet Transform ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Satellite Images ◽  
Satellite Image ◽  
Resolution Enhancement ◽  
Gaussian Mixture ◽  
Lifting Wavelet

From the last few decades, Satellite images are being used widely in various applications like monitoring of forest areas, weather forecasting, polar bears counting, etc. In those applications to get more details of images efficiently, satellite images should be enhanced up to the required level as the images captured by the satellites are covered very large areas and those are very low-resolution images due to the high altitudes of satellites from the earth. We proposed a method of an image enhancement which includes both resolution enhancement and contrast enhancement. In this method, Stationary Wavelet Transform (SWT) in combination with Lifting Wavelet Transform (LWT) is used for image decomposition into low-frequency sub band images and high-frequency sub band images to separate smooth regions and sharp edges to interpolate regions and edges separately to reduce blurring effect in edges and noise in smooth regions. To get smoother details and sharper edges, Gaussian Mixture Model (GMM) is used for interpolation in resolution enhancement process and SWT with the combination of Contrasts Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement process. SWT in combination with LWT improves the resolution effectively and also minimizes the execution time drastically than existing methods due to the shift invariance of SWT and reduced computations in LWT and GMM interpolation results from sharper edges and smoother details. SWT is used in combination with CLAHE to enhance the contrast and mitigate the noise effects than existing methods. The proposed method gives superior results and compared with existing techniques with PSNR, Noise Estimation, and visual results

scholarly journals Multiple-Antenna Cooperative Spectrum Sensing Based on the Wavelet Transform and Gaussian Mixture Model

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3863
Author(s):  
Shunchao Zhang ◽  
Yonghua Wang ◽  
Hantao Yuan ◽  
Pin Wan ◽  
Yongwei Zhang
Keyword(s):  
Wavelet Transform ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Spectrum Sensing ◽  
Channel Model ◽  
Cooperative Spectrum Sensing ◽  
Gaussian Mixture ◽  
Multiple Antenna ◽  
Two Dimensional ◽  
Statistical Features

Spectrum sensing is a core technology in cognitive radio (CR) systems. In this paper, a multiple-antenna cooperative spectrum sensor based on the wavelet transform and Gaussian mixture model (MAWG) is proposed. Compared with traditional methods, the MAWG method avoids the derivation of the threshold and improves the performance of single secondary user (SU) spectrum sensing in cases of channel loss and hidden terminal. The MAWG method reduces the noise of the signal which collected by the multiple-antenna SUs through the wavelet transform. Then, the fusion center (FC) extracts the statistical features from the signals that are pre-processed by the wavelet transform. To extract the statistical features, an sensing data fusion method is proposed. The MAWG method divides all SUs that are involved in the cooperative spectrum sensing into two clusters and extracts a two-dimensional feature vector. In order to avoid complicated decision threshold derivation, the Gaussian mixture model (GMM) is used to train a classifier for spectrum sensing according to these two-dimensional feature vectors. Simulation experiments are performed in the κ - μ channel model. The simulation shows that the MAWG can effectively improve spectrum sensing performance under the κ - μ channel model.

scholarly journals Gaussian mixture model-based contrast enhancement

2015 ◽  
Vol 9 (7) ◽  
pp. 569-577 ◽  
Author(s):  
Mohsen Abdoli ◽  
Mohammad Ghanbari ◽  
Hossein Sarikhani ◽  
Patrice Brault
Keyword(s):  
Contrast Enhancement ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Model Based

scholarly journals Satellite Image Enhancement using Optimized Wavelet Decomposition and Bicubic Interpolation

2020 ◽  
Vol 8 (5) ◽  
pp. 4430-4434
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Satellite Images ◽  
Weather Forecasting ◽  
Satellite Image ◽  
Resolution Image ◽  
Military Applications ◽  
Bicubic Interpolation

Satellite Images (SI) play a vital role in various civilian and military applications for weather forecasting, monitoring of resources of the earth, environmental studies, observing natural disasters and natural calamities, etc. When these SI are used in military applications and almost all other applications for efficient study, the big challenge is its resolution. In wavelet transforms based satellite image enhancement techniques, choosing a proper wavelet transform plays a key role and vary with the image to image. To improve the resolution, a novel robust optimized wavelet decomposition and a bicubic interpolation-based satellite image enhancement method is proposed. In this method, the Stochastic Diffusion Search (SDS) algorithm is used to get the optimized wavelet decomposition of the image into different subbands and bicubic interpolation is used to improve the resolution. Image is decomposed using the optimized wavelet filter bank based on the SDS algorithm, decomposed sub-bands are interpolated with bicubic interpolation and inverse wavelet transform is applied to compose the interpolated sub-bands into a high-resolution image. The proposed method is tested on satellite images and other images also. Compared to the proposed method with the existing methods and proved that the proposed method is superior to existing methods and applicable to any type of image.

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