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Image restorations with the Richardson-Lucy algorithm suffer the usual drawback imposed by the constraint of a constant Point Spread Function - PSF as unfolding function. Indeed, even when the image exhibits a constant spatial resolution over its whole surface, an important aspect is that as the iterations advance, the overall resolution is improved while the PSF remains constant. This work proposes an algorithm which restores images by the Richardson-Lucy (RL) algorithm, using however, a varying PSF as the iterations proceed. For this purpose, the PSF width is reduced to cope with the last-achieved image resolution and the next iteration would be carried out with it. The process is repeated until the PSF does not change significantly. A main point in this procedure is how to evaluate the PSF tied to the image resolution. In this work this is performed on the grounds that the global contrast increases with the resolution improvement, for many gray pixels migrate towards darker or brighter regions. Hence, deconvolving an image with a steadily increasing PSF width, somewhere a maximum global contrast would be reached, corresponding to the best PSF. Synthetic, as well as experimental images deconvolved with the proposed technique, outperform the final quality of the same ones treated with the original Richardson-Lucy algorithm for any number of iterations. The algorithm and ancillary procedures have been embedded into an ad hoc written Fortran 90 program capable to generate synthetic images and process them and the real ones.
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