结合自适应核回归和全变差的乘性噪声去除

Multiplicative noise removal via adaptive kernel regression and total variation

为了更好地去除图像中的乘性噪声,提出一个新的三阶段乘性噪声去除算法。第一阶段在图像的对数域用自适应的掌舵核回归(SKR)对图像进行去噪处理;第二阶段用全变差(TV)方法对第一阶段处理的结果进行补充处理;第三阶段通过指数变换和误差纠偏,把图像变回到真实的图像域。新方法具有掌舵核回归与全变差两种方法的优点,实验结果证明了其去除乘性噪声的有效性。

To remove the muhiplicative noise better, a new three-stage method for muhiplicative noise removal was proposed. In the first stage, log-image was processed by adaptive Steer Kernel Regression （ SKR）. Then in the second stage, the Total Variation （TV） regularization method was used to amend the image obtained. At last, via an exponential function and bias correction, the result was transformed back from the log-domain to the real one. The new method combined the advantages of steer kernel regression and total variation method. The experimental results show that the new method is more effective to filter out multiplicative noise.