抽象的

Despeckling of SAR Images Based on Bayes Shrinkage Thresholding in Shear let Domain

J.Sivasankari, M.Maritta Ashlin, T.Janani, D.Farehin Shahin

Synthetic Aperture Radar (SAR) is widely used for obtaining high-resolution images of the earth.SAR Image processing is greatly affected by speckle noise .The despeckling process of SAR image where speckle may interfere with automatic interpretation, which can further affect the processing of SAR image. Synthetic Aperture Radar (SAR) image is easily polluted by speckle noise. The speckle reduction of SAR images is based on spatial filter, Wavelet transform, Curvelet Transform, where the smoothening of image is difficult to achieve. Inorder to achieve an improvised quality in image the despeckling is done by using shearlet Domain. Thisproject introduces the effective speckle reduction of SAR images based on a new approach of Discrete Shearlet Transform withBayes Shrinkage Thresholding. The shearlet domain turns out to be a powerful tool for image enhancement in fine-structured areas. This model allows us to classify the shearlet coefficients into classes having different degrees of heterogeneity, which can reduce the shrinkage ratio for heterogeneity regions while suppresses speckle effectively to realize both despeckling and detail preservation. The combined effect of soft thresholding in Shearlet Transform works better when compared to the other spatial domain filter and transforms. It also performs better in the curvilinear features of SAR images.