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
Handwriting image enhancement using local learning windowing, Gaussian Mixture Model and k-means clustering
