Wavelets have been intensely used for astronomical image restoration during the last 20 years. However, wavelets have shown some limitations for images containing complexe texture features that can find in cosmic string maps or planetary images. We propose to use recently developed dictionary learning techniques to overcome those limitations. We address here the problem where a white gaussian noise is to be removed from an image. The original image is assumed to be sparsly represented in a dictionary which is learned during the denoising. Patch averaging has proven to be an efficient way to combine local sparsity constrain and a global Bayesian treatment and is applied here to process astrophysical image compared to classic wavelet shrinkage and associated techniques.