Authors: Tomáš Lukeš, Daniel Kert, Karel Fliegel, Miloš Klíma
We propose a novel binarization method based on a signal reconstruction using an iterative detection network. The algorithm simulates the whole image acquisition process taking into account a point spread function of the imaging system and its noise characteristics. The negative influence of image blur and noise is effectively suppressed by iterative detection network based on the criterion of maximum a posteriori probability. The proposed method was successfully applied to noisy microscopy images. Experiments show that the proposed method due to the noise suppression and deconvolution properties provides for noisy images significantly better results compared to common thresholding techniques. Binarized images obtained by the proposed method can be particularly useful for particle detection and analysis of cell samples.
We propose a novel binarization method based on a signal reconstruction using an iterative detection network.
Our method simulates the whole image acquisition chain and evaluates probabilities of all possible values of each pixel.
The iterative detection network decides for each pixel whether its value will be black or white by maximizing the corresponding a posteriori probability that the pixel contains the useful signal (object in the foreground) or not (background noise etc.).
The problem can be seen as a decoding task similar to the decoding of one-dimensional communication signals.
The iterative detection network is composed from functional blocks which performs soft inversion .
IEEE International Conference on Image Processing – ICIP2014, October 27-30, 2014: CNIT La Défense, Paris, France