Research on wavelet-based contourlet transform algorithm for adaptive optics image denoising

In this paper, we present a wavelet-based contourlet transform (WBCT) method to adaptive optics (AO) image denoising. This method is implemented through combining with BayesShrink theory to estimate the threshold and then improving the adaptive method of selecting threshold, finally obtaining the optimal threshold. The WBCT transform coefficients of different decomposition scales and different direction to select the adaptive optimal threshold to achieve denoising. We evaluate our algorithm using the DWT-NABayesShrink algorithm, DTCWT-BayesShrink algorithm and CbATD algorithm as a benchmark. Using simulated and real observed AO images, we show that our approach with WBCT algorithm exhibits better performance both in peak signal-to-noise ratio (PSNR) and visual quality, which opens up many perspectives for AO image denoising in the astronautics field.