. . . "2014-10-27+01:00"^^ . . . . . . "21230" . "IEEE" . . "2014 IEEE International Conference on Image Processing (ICIP 2014)" . "Binarization of noisy microscopy images through signal reconstruction using iterative detection network"@en . "4"^^ . "[C1153731B7F4]" . . "Kl\u00EDma, Milo\u0161" . . . . . . "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."@en . "Par\u00ED\u017E" . . . "Luke\u0161, Tom\u00E1\u0161" . "4"^^ . "Fliegel, Karel" . "Binarization of noisy microscopy images through signal reconstruction using iterative detection network"@en . "Binarization; Microscopy Images; Image Processing; Image Reconstruction; Iterative Detection Network"@en . "5277" . "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." . "Piscataway" . . "Binarization of noisy microscopy images through signal reconstruction using iterative detection network" . . "RIV/68407700:21230/14:00222807" . "978-1-4799-5750-7" . "4"^^ . "P(GAP102/10/1320), P(LD12018), S" . . . . "RIV/68407700:21230/14:00222807!RIV15-MSM-21230___" . . "Kekrt, Daniel" . "Binarization of noisy microscopy images through signal reconstruction using iterative detection network" .