Atmospheric Turbulence Image Denoising Algorithm Based On Wavelet-Domain Curvelet Transform

To enhance the spatial resolution of the atmospheric turbulence image, an atmospheric turbulence image denoising algorithm based on wavelet domain Curvelet transform (WDCT) is proposed in this paper. This algorithm bases on the statistical property of the image noise and combines with Bayes Shrink theory to optimize threshold selecting. Firstly, the turbulence degraded image is performed to a single 2-D discrete wavelet transform (2-D DWT), then extracts the high frequency coefficients and make the fast discrete Curvelet transform for the degraded image. Finally, we estimate the threshold value T according to the Bayesian criterion, and improve the adaptive method of selecting threshold, obtain the optimized threshold. Therefore, the implementation process of the proposed algorithm is addressed. In order to verify the effectiveness of the proposed denoising method, basing on the objective evaluation that are the peak signal to noise ratio (PSNR) and mean square error (MSE), a series of denoising experiments are performed on simulated images and practical observed turbulence image. The experiment results show that, compared to DWT-NABayesShrink method and UDWT method, the visual effect is better, PSNR value has improved 7.27% and 4.92%, respectively, and MSE value are degraded 26.3% and 23.1%, respectively. Our algorithm can obtain the clear image, so the research results have application values for turbulence image denoising work.