An improved Wienerchop algorithm for image denoising

Empirically designed wavelet domain Wiener filters such as WienerChop have superior performance over other denoising algorithms using wavelet thresholding. An effective way to improve the denoising performance of WienerChop algorithm lies in the estimation precision of the expected signal. The paper proposed a way in which the Bayesian based wavelet thresholding denoising technique is adopted to estimate the desired signal in the first wavelet domain to ensure the better estimation. Theoretical analysis and simulation results show that our method outperforms the traditional WienerChop algorithm, while the main features of the latter such as simplicity and speed are also preserved.