The paper presents the wavelet shrinkage and the image compression for SAR images, based on discrete wavelet transform (DWT). It is very efficient to integrate these two procedures in a single process. First, a speckled SAR image is transformed by using multiple level wavelet decomposition. The variance of noise is estimated from wavelet coefficients to detennine the threshold, which is used for soft thresholding in all high frequency subbands. The well-known threshold estimation includes SimpleShrink, NormalShrink, VisuShrink, SureShrink, and BayesShrink. The obtained wavelet coefficients are then encoded by using etnbedded zero-tree wavelet (EZW) to produce the output bit stream of the despeckled image. By means of an evaluating technique include S/MSE, MSE, and PSNR. Experimental results on JERSl/SAR images are also given.
Low bit rate image coding is essential for many visual communication applications. However. it yields visually annoying artifacts that highly degrade the perceptual quality of image data. In this paper we propose a novel method for reducing the blocking artifact that occurs when using DCT image coding at low bit rates. The blocking artifact has varied visibility in different region. that is it is more visible in a smooth region than detailed region such as textures and edges. Hence, the proposed method is based on image segmentation and then thresholding the coefficients of different regions with adaptive thresholds. The proposed method gives consistent improvement over the previous methods for deblocking in term of peak signal-to-noise ratio (PSNR). edge variance (EV). and visual quality.
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