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Image mosaicing is a popular way to obtain a wide field of view image of a scene
while preserving the details. This is especially true when studying the structure
of the neural system. One special feature of the synaptic tree is its huge ratio
between the dendrites’ size and the tree’s spread. Thus, it is crucial to have a
wide field of view in keeping a high image-resolution. After assembly, the mosaics
must be de-convoluted to attenuate the effects due to the defocalisation.
However, discontinuities induced by acquisition-system side effects, or lacking
images can provoke the de-convolution program to fail.
We present in this work several tools to remove such issues and we show some results
of a mosaic being assembled according to these methods.
Due to the very nature of bright field microscopy, the illumination on the sample is
not evenly distributed. Assembling images without any correction induces huge seam
effects. We propose to dispose of such seams in fitting on the input images a paraboloid
and subtracting it from those images. We called this method background suppression.
In our experiment, we subtracted to each input image the same paraboloid, which proved to bring the best results.
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Figure. On the left, a "degraded Lena". We simulated the artefacts of the mosaicing of the microscope on Lena. The image is divided into 16 cells, of which two are missing. A non-uniform background has been added to each cell to simulate the spherical aberration and the non-uniformity of illumination.
On the right, we show the result after processing. The discontinuities are almost invisible.
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