| With the development of modern remote sensing technology, more and more multi-spatial and multi-spectral resolution remote images get the rapid development and wide application. An image system consists of acquisition, transmission, reception and display, every part of which can be disturbed, so that the image equality can be degraded. Elementary requirement of image processing is how to deal with these degraded images to meet our need.Synthetic Aperture Radar (SAR) plays an important role in modern remote sensing. It was broadly applied in almost every field of economy and society sustainable development because they can be obtained anytime, any-weather, and with high resolution. Spectral remote image get wide application because it take on favorable spectral characteristics. But SAR image is liable to be affected by speckle noise, while almost spectral image have shortcomings of low contrast between object and background, edge blurring. Therefore, to get a more correct, clear and exact understanding about the remote images, and to synthesize all kinds of image data. it is importance for us to find a way to process remote image such as de-noise, enhancement and fusion. Image noise reduction, image enhancement and image fusion based on multi-resolution wavelet analysis are studied in this thesis. The main works are as follows:Firstly, in order to produce quincunx down sampling and up sampling which could form directional wavelet transform, the rotated factor matrixes is introduced based on discrete wavelet transform. Image decomposition with directional wavelet transform has polarization characteristics. And apply it to the image de-speckling, enhancement and fusion.Secondly, a soft-threshold mapping function is proposed during reducing remote image speckle noise. Different soft-threshold gain were used to different scale high frequency sub-band image use, and a new algorithm for wavelet coefficients threshold is applied to reduce speckle noises based on high frequency... |
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