Fast regularization technique for expectation maximization algorithm for optical sectioning microscopy
نویسندگان
چکیده
Maximum likelihood image restoration is a powerful method for three-dimensional (3D) computational optical sectioning microscopy of extended objects. With punctate specimens, however, this method produces a few very bright isolated spots and dim detail around them is lost. The commonly used regularization methods (sieves and roughness penalty) decrease the amplitude of the bright spots, but do not avoid loosing dim detail. We derived an intensity regularization that decreases the amplitude of bright spots without loosing dim detail. In contrast with other regularization methods, this method does not increase significanlty the computational complextity of the estimation algorithm.
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