scholarly journals Optimized Wavelet Decomposition and Gaussian interpolation based Satellite Image Enhancement

2020 ◽  
Vol 9 (4) ◽  
pp. 2609-2612
Keyword(s):  
Wavelet Transforms ◽  
Wavelet Decomposition ◽  
Search Algorithm ◽  
Satellite Image ◽  
Resolution Enhancement ◽  
Vital Role ◽  
Discrete Wavelet ◽  
Sensing Applications ◽  
Enhancement Method ◽  
Gaussian Interpolation

Satellite images (SI) play a vital role in various remote sensing applications like geoscience, geographical studies, observing the earth's atmosphere, monitoring natural disasters, etc. The SI are used in these applications require high-resolution. The performance of the wavelet transforms based resolution enhancement methods depends on the type of the mother wavelet used and it varies with image to image. The novel robust SI resolution enhancement technique including Optimized wavelet transform based image decomposition and Gaussian interpolation is proposed in this paper. Optimized wavelet decomposition is obtained using the Stochastic Diffusion Search algorithm and the Gaussian distribution function is used for interpolation. The proposed method is compared with the Discrete wavelet decomposition and Gaussian interpolation resolution enhancement method and proved that the proposed method gives the best results for any image.

scholarly journals An approach for image Contrast and Resolution Enhancement based Wavelet Transform by incorporating Cuckoo Search and SVD algorithms

2019 ◽  
Vol 8 (11) ◽  
pp. 24858-24868
Author(s):  
Sandeepa K S ◽  
Basavaraj N Jagadale ◽  
J S Bhat
Keyword(s):  
Wavelet Transform ◽  
Singular Value Decomposition ◽  
Search Algorithm ◽  
Resolution Enhancement ◽  
Cuckoo Search ◽  
Singular Value ◽  
Image Contrast ◽  
Discrete Wavelet ◽  
Maximum Value ◽  
Value Decomposition

Image enhancement techniques are prominently used to analyze the image by enhancing key factors like contrast, resolution, and quality of the image. With the proper analysis of images, it is desirable to pre-process the image for resolution and contrast enhancement. We present here a new approach based on discrete wavelet transform (DWT), singular value decomposition (SVD) for image contrast and resolution enhancement, The contrast of the image is enhanced by maximum value fusion technique applied to the images created by using modified cuckoo search algorithm (CSA) and singular value decomposition separately. The masking approach is employed, for obtaining residual pixel value between original and scaled images independently. The resolution of the image is enhanced by combining interpolated high-frequency sub-band and maximum value fusion images. The proposed algorithm helps to minimize the noise artifacts and over enhancement problems. Experimental results are tested in terms of peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE). The proposed method shows better performance compared to other contrast and resolution enhancement techniques.

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.

scholarly journals Satellite Image Resolution Enhancement based on Dual-Domain Filtering

2018 ◽  
Vol 7 (2.7) ◽  
pp. 466
Author(s):  
K Rasool Reddy ◽  
Dr K.NagaPrakash ◽  
Dr K.Prasanthi Jasmime ◽  
M Tulasidas
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Resolution Enhancement ◽  
Main Idea ◽  
Linear Interpolation ◽  
Vital Role ◽  
Image Resolution ◽  
Disaster Mitigation ◽  
Source Image ◽  
Dual Domain

Satellite images place vital role in agriculture, Disaster mitigation and geosciences applications. Satellite images include both spatial and temporal resolution, in that spatial resolution is influence the accuracy of ground objects. The main idea of this work is to enhance the resolution of satellite images. In this work, a dual domain filtering based approach is introduced for resolution enhancement (RE). Initially, the source image is subdivided into approximation and detail coefficients by Stationary Wavelet transform (SWT). The detail coefficients are interpolated based on bi-linear interpolation. The interpolated detail coefficients are applied to Non-Local Means (NLM) filter to minimize the artifacts produced by SWT. The filtered detail and approximation coefficients of source image are fed to ISWT to attain high resolution (HR) image. The proposed system is superior to other existing strategies like DWT-NLM and DWT-SWT. 

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