New Effective Filter in the Spatial Domain for Speckle Noise Reduction
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Fig. 10 The dependence of the standard deviation of filtration errors on the deformation value
Fig. 11 The dependence of calculation time on the number of polynomials (for representation of
2D sine function with dimension 1000 × 1000)
5 Conclusion
The proposed method showed better quality and less error than the filter in the
frequency domain, but it requires more processing time. However, this drawback is
not so critical. The ability to cut off narrow intense peaks eliminates speckle noise
even from periodic high-frequency structures with virtually no distortion. The next
step of our work is to improve the capabilities of the new filter by introducing twodimensional Chebyshev polynomials to filter complex phase maps. In addition, the
possibility of applying the proposed method to filter digital holograms from the
speckle structure to eliminate the diffraction halo is considered.
187
Fig. 10 The dependence of the standard deviation of filtration errors on the deformation value
Fig. 11 The dependence of calculation time on the number of polynomials (for representation of
2D sine function with dimension 1000 × 1000)
5 Conclusion
The proposed method showed better quality and less error than the filter in the
frequency domain, but it requires more processing time. However, this drawback is
not so critical. The ability to cut off narrow intense peaks eliminates speckle noise
even from periodic high-frequency structures with virtually no distortion. The next
step of our work is to improve the capabilities of the new filter by introducing twodimensional Chebyshev polynomials to filter complex phase maps. In addition, the
possibility of applying the proposed method to filter digital holograms from the
speckle structure to eliminate the diffraction halo is considered.