tokens: in Appendix D , TVL2 seems to be the best method, closer to the noiseless image reported in the last column of all the figures, followed by starlet, b-UWT(7/9), and b-UWT(7/9)+Wiener, which also produce good results. The PM, TV Chambolle, and BM3D are often very similar from a qualitative point of view. Most of the time, the flux of the objects detected by TVL2 is highly concentrated (see, e.g., Figs. D.2 and D.5 ), but in some cases the objects flux is distributed on a larger area, as visible for example, in Fig. D.6 . Starlet produces, in some cases, bright isotropic objects, which could lead to misleading results in terms of their morphological information (see, e.g., Figs. D.14 and D.16 ). In looking at Fig. D.10 , PSF seems to wrongly detect two objects, when other denoising methods detect them correctly. This qualitative analysis is confirmed by the corresponding quantitative analysis made, as visible in Fig. 8 . When more than one object is present in the figures, sometimes the different schemes are not capable of recognizing all the objects (see, e.g., Figs. D.3 , D.6 , D.9 , D.11 and D.13 ). Moreover, fluctuations of the background around the objects are not completely removed and the smoothing can create artifacts (see, e.g., Fig. D.13 , where an elliptical galaxy seems to appear as a spiral galaxy after denoising). b-UWT(7/9), and b-UWT(7/9)+Wiener are susceptible to background fluctuations and end up creating visual artifacts (see Figs. D.17 and D.18 ). When inspecting the snapshots from real images, a qualitative analysis is inconclusive, so we have to stick to the quantitative analysis in Fig. 12 . In fact, looking at the crops reported in Appendix E , the PM and bilateral are often so similar that it is difficult to distinguish them, although, based on Fig. 12 , the differences between them are clear (the PM performs worse than the bilateral). An analogous remark can be made for BM3D, which produces images similar to the PM and bilateral, as visible, for example, in Fig. E.12 . Here, the TVL2 is, again, the most promising method, which is visually close to the HUDF09 also thanks to the automatic optimization of its internal parameters, and this is confirmed by the results reported in Fig. 12 . Images from starlet, b-UWT(7/9), and b-UWT(7/9)+Wiener are quite similar to each other, even though those from starlet present fewer visual artifacts (see Figs. E.10 , E.12 – E.14 ). Differently from the other methods, NL-means produces nearly squared patches of uniform flux, which effectively reduce noise fluctuations (see Figs. E.8 and E.9 ), but also could lead to artifacts (see, e.g., Fig. E.5 , where the galaxy in the bottom-right corner assumes a nearly boxy-shape). Even if artifacts are generated and the morphology of the objects detected could be altered, we tested the efficiency of the algorithms in preserving the FWHM and the ellipticity of the objects in Sect. 5.6 , proving that on the average the shapes are preserved. A
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