Increase the Quality of Remote Sensing Images Using Dwt Fuzzy & Gaussian Mixture Model

High Resolution Satellite Images are of a significant importance in many fields of research. For the last few decades Wavelets are playing a key role in image resolution enhancement technique. Gaussian component densities instead of weighted sum of neighborhood pixels such as bicubic or bilinear interpolations. There are numerous ripple domain primarily based strategies like AHE, separate ripple remodel, CLAHE, distinction and Stationary ripple transform. In this paper we use DWT transforms, these transforms decompose the given image into four sub-bands, out of that one is of low frequency and therefore the rest are of high frequency. It is proved with the quantitative (peak signal-to-noise ratio and quality index) and visual results over the conventional and state-of-art image resolution enhancement techniques. The HF parts are interpolated using typical interpolation techniques. Then we use IDWT to mix the interpolated high frequency and low frequency parts.