III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.(a)Figure 2. ISNR values for the restored images.(b)Iteration RL RLA RLM0 0.0 0.0 0.0205 5,8919† 6,6681 6,6682241 5,8545 6,7095 6,7093†242 5,8525 6,7096† 6,70925000 -7,7216 -7,4090 -7,4084Table 1. ISNR values for the restored imagesconsidering the best results.(c)Figure 1. (a) section of a 3D phantom image; (b)blurred image section; (c) blurred and noisy imagesection.bels) by‖f − u‖ 2IS NR = 10 · log 10‖f − ˆf‖ , (11)2where f, u, and ˆf are defined as above.Figure 2 presents the ISNR values for the restored imagesconsidering 3000 iterations in each experiment. Thebest values found for the ISNR were in 205 iterations to theRL algorithm, 242 iterations to the RL with the Anscombeprocedure (RLA), and 241 iterations to the RL with the medianfilter (RLM). These results can be visualized in table 1,where a † symbol denotes the best result in each case. TheISNR values decreased to higher iteration numbers.From figure 2 and from table 1, we conclude that accordingto the ISNR criteria, the proposed method was able toproduce better results than the RL algorithm without pre-processing of the noise. We also note that better results areachieved in a similar number of iteractions.The restored image using the RL algorithm is presentedin figure 3(a). Figure 3(b) presents the restoration result forthe RLM algorithm, and figure 3(c) presents the result usingthe RLA procedure.Although both the RLA and the RLM algorithms producevery similar results considering the ISNR criteria, itis important to note that the median filter is an ad hoc procedurefor noise reduction. On the other hand, the methoddescribed in section 3.1 takes into account the statistical natureof the noise. Futhermore, it is a pointwise procedureand it has a computational complexity compared to the medianfilter.5. Concluding RemarksWe have presented an algorithm for deconvolution microscopythat is able to produce better results than theRichardson-Lucy algorithm. It consists of reducing thePoisson noise in the observed image before the applicationof the Richardson-Lucy procedure. In future works,we intent to extend the method considering a total variationregularization approach.136