Proceedings Article | 6 September 2019
KEYWORDS: Image filtering, Digital filtering, Electronic filtering, Anisotropic filtering, Confocal microscopy, Radio over Fiber, Nonlinear filtering, Filtering (signal processing), Smoothing, Image processing
Confocal microscopy is a widely used tool in the biomedical area, allowing to obtain 3D images with a high spatial resolution. Despite having advantages over conventional microscopy, the analysis of cellular images through confocal microscopy is a complicated process due to the very low S/N, so the use of filters is necessary to reduce noise. However, this step normally affects the quality of the edges, making them more diffuse. In addition, images acquired in confocal microscopy are affected by distortions introduced by lenses and the acquisition system. Therefore, it is possible to improve the edges definition by eliminating distortions. This is done by means of deconvolution methods such as the Wiener filter. Furthermore, in recent years a new generation of smoothing filters have been developed that seek to reduce Gaussian-type noise, without losing edges. However, a study has not been carried out to determine if these filters can be used in the elimination of noise in confocal microscopy, which are contaminated with Poisson noise. Therefore, in this work we present a comparative study of ten filters for the elimination of noise in confocal microscopy: median, anisotropic diffusion, bilateral, propagated, improved propagated, Rudin-Osher-Fatemi (ROF), TVL1, non-local means, K-SVD, and Wavelet 'A trous' and Haar filters, with and without preprocessing images with the Wiener filter, taking as criteria the noise reduction and the conservation of edges.
