The goal of image denoising algorithms is to reduce the contribution of the observational noise intrinsic to the images, preserving information (e.g. small sources, morphological details etc.). State-of-the-art mathematical algorithms have been developed and successfully applied in many fields such as medical imaging, video surveillance, satellite observations of the earth. Although numerous types of denoising algorithms exist, a detailed evaluation of their impact on astronomical images is currently lacking, with few examples of denoising applied to astronomical data found in the literature. Testing different algorithms is then fundamental to make a reasonable comparison, especially it is of interest to understand their performance with respect to faint and distant astronomical objects (e.g. galaxies in the early Universe) which are predominant in extragalactic images. I will show how some of these algorithms can enhance the analysis quality for this type of images and improve performances of future astronomical surveys.