Restoration of fluorescence microscopic images using a nonlinear PDE based filter

In this paper, a nonlinear anisotropic diffusion based filter adapted to Poisson noise is proposed to restore the degraded fluorescence microscopic images due to Poisson noise. The proposed filter is based on a combined maximum a posterior (MAP) and partial differential equations (PDE) based approach to the image reconstruction problem. The performance of the proposed scheme has been compared with other standard techniques available in literature such as Wiener filter, regularized filter, Lucy-Richardson filter and another proposed nonlinear complex diffusion based filter in terms of mean square error (MSE), peak signal-to-noise ratio (PSNR), correlation parameter (CP) and mean structure similarity index map (MSSIM). The obtained results shows that the proposed complex diffusion based filter adapted to Poisson noise performs better in comparison to other filters and is an optimal choice for reduction of intrinsic Poisson noise from the fluorescence microscopic images as well as other digital images corrupted with Poisson noise and it is also well capable of preserving edges and radiometric information such as luminance and contrast of the restored image.