Satellite Image Enhancement using Discrete Wavelet Transform, Singular Value Decomposition and its Noise Performance Analysis

This paper introduce a new concept of satellite image resolution and contrast enhancement technique when the image is suffered from the noise and filtering it by various types of filters then the image is processed by discrete wavelet transform (DWT) and singular value decomposition (SVD) to get new modified contrast and resolution enhanced image. Satellite images are used in many applications such as geosciences studies, astronomy, and geographical information systems. Two most important quality factors of images are contrast and resolution, here this technique decomposes the input filtered image into the four frequency sub-bands by using DWT and then the high frequency subband images and input image have been interpolated along with this the technique also estimates the singular value matrix of the low-low sub band of histogram equalized image and input filtered image then normalize both singular value matrices to obtain brightness enhanced image. In, order to get the new image of better contrast and resolution all these subbands are combined using inverse DWT. The following procedure is done with different types of noises and different types of filters then they are compared with conventional image equalization techniques such as general histogram equalization (GHE), local histogram equalization (LHE) and also from state-of-the-art technique which is singular value equalization (SVE) and Discrete Wavelet Transform (DWT) and the experimental results show the supremacy of the proposed method over conventional and state-of-art techniques.