The performance of high-resolution imaging with large optical instruments is severely limited by atmospheric turbulence. Adaptive
optics (AO) offers a real-time compensation for turbulence. However, the correction is often only partial, and image restoration is
required for reaching or nearing to the diffraction limit. Wavelet-based techniques have been applied in atmospheric turbulencedegraded
image restoration. However, wavelets do not restore long edges with high fidelity while curvelets are challenged by small
features. Loosely speaking, each transform has its own area of expertise and this complementarity may be of great potential. So, we
expect that the combination of different transforms can improve the quality of the result. In this paper, a novel deconvolution
algorithm, based on both the wavelet transform and the curvelet transform (NDbWC). It extends previous results which were obtained
for the image wavelet-based restoration. Using these two different transformations in the same algorithm allows us to optimally detect
in tire same time isotropic features, well represented by the wavelet transform, and edges better represented by the curvelet transform.
The NDbWC algorithm works better than classical wavelet-regularization method in deconvolution of the turbulence-degraded image
with low SNR.