An important application domain of the wavelet theory is compression. In this paper, we use wavelet transforms to compress two different types of images (i) medical images (Echo image), (ii) color images, by using two different procedures. We use different types of wavelet transforms. Further, the compression ratio, the bits per pixel and the relative 2-norm difference are calculated. The quantitative measures are used to compare and contrast the performance of different wavelet transforms.
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An important application domain of the wavelet theory is denoising. In this paper, we use the wavelet transforms to denoise the medical images. There are many kinds of noise and we study only three types; i) additive random noise; ii) pop noise and; iii) localized random noise. Further, we use Root Mean Square Error(RMSE) and Signal to Noise Ratio (SNR) to measure the error between a noisy image and the original image.
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