A Study To Wavelet Soft-Thresholding Algorithm With Application To SAR Image De-Noising

With the raPid development of sateilite astronomy technique, Syntheticaperture radar (SAR) is wde1y app1ied not only in military field, but aiso in theagricuitural, geograPhic, oceanic, weather etc. fields. However, for itsinterterence imaging, observed images are contaminated by specMe noise. It isa1ways a hot yct hard topic for effectively suppressing speck1e noise of SARimage in the remote sensing image processing.The multi-resolution analysis (MRA property of the wave1et can make it notonly effectively reduce noise, but a1so we1l suited preserve edge structure.Waveletist such as Mallat, Witkln and Donoho propo$e the wavelet de-noisingmethods. Especia11y, the wave1et g1obal soft-thresho1ding (WGST) de-noisingapproach put formrd by Donoho in 1995, for it is simple yet effective tode-noise the additive white gaussian noise, has been wide1y spread. However,the signal of SAR image is complicatedly di$tributed in every subband, and themodel of sPeckle noise is uncertain. So direct1y using 2DItwo--dimension)extending a1gorithm of the 1D(one dimension) soft-thresho1ding for smoothingspeck1e noise, noise cannot be effective1y reduced, and also, high-frequencystructure wtll severely be lost.In the paper, our research work introduces WGST algorithm to SAR imagede-noising for speckle noise, and improve it to be aPplied in the remote sensingimage processing (improved WGST, ca1led wave1et 1ocal ST, WLST). At the sametime, propose a thresho1d formula for the WLST algorithm. Our research work as2xl7X#,%II8g@tf&&zfollows:Firstly, We otend the WGST algOrithm from 1D signa1 to 2D one, andcarefully analyze the SAR images, and then give the de-noised andedge-edracted eXPerimental resu1ts using the 2D WGST.Second1y, because speck1e noise is distributed complicatedly in themulti-resolution and mu1ti-orientation high-frequency coefficients, an improved, algorithm is put forward, which lies in selecting 1eve1- o ri e nt ati o n- d ep en d entthresholds.Finally, Assuming speck1e noise as a near gaussian model, a novel thresho1dformula, well suited to the improved algorithm, is proposed. And themultiplicative speckle noise is transformed into an additive one by thelog8rithmic transformation periormed on SAR image, and then the WLSTa1gorithm is applied for speckle noise reduction.The experimental results and data ana1ysis show that it is effective for WLSTa1gorithm app1ied in SAR image for speck1e noise removal and edge preserving.Hence the improved algorithm WLST can be effectively used in SAR image withspeckle noise.