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ArrayFire: iterativeDeconv
Iterative Deconvolution. More...
Iterative Deconvolution.
The following table shows the iteration update equations of the respective deconvolution algorithms.
Algorithm Update Equation LandWeber \( \hat{I}_{n} = \hat{I}_{n-1} + \alpha * P^T \otimes (I - P \otimes \hat{I}_{n-1}) \) Richardson-Lucy \( \hat{I}_{n} = \hat{I}_{n-1} . ( \frac{I}{\hat{I}_{n-1} \otimes P} \otimes P^T ) \)
where
- \( I \) is the observed(input/blurred) image
- \( P \) is the point spread function
- \( P^T \) is the transpose of point spread function
- \( \hat{I}_{n} \) is the current iteration's updated image estimate
- \( \hat{I}_{n-1} \) is the previous iteration's image estimate
- \( \alpha \) is the relaxation factor
- \( \otimes \) indicates the convolution operator
Iterative deconvolution function excepts af::array of the following types only:
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Note
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The type of output af::array from deconvolution will be double if the input array type is double. For other types, output type will be float. Should the caller want to save the image to disk or require the values of output to be in a fixed range, that should be done by the caller explicitly.
◆ af_iterative_deconv()
C Interface for Iterative deconvolution algorithm.
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Parameters
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[out] out is the sharp estimate generated from the blurred input [in] in is the blurred input image [in] ker is the kernel(point spread function) known to have caused the blur in the system [in] iterations is the number of iterations the algorithm will run [in] relax_factor is the relaxation factor multiplied with distance of estimate from observed image. [in] algo takes value of type enum af_iterative_deconv_algo indicating the iterative deconvolution algorithm to be used
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Returns
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AF_SUCCESS if the deconvolution is successful, otherwise an appropriate error code is returned.
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Note
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relax_factor argument is ignore when it AF_ITERATIVE_DECONV_RICHARDSONLUCY algorithm is used.
◆ iterativeDeconv()
C++ Interface for Iterative deconvolution algorithm.
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Parameters
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[in] in is the blurred input image [in] ker is the kernel(point spread function) known to have caused the blur in the system [in] iterations is the number of iterations the algorithm will run [in] relaxFactor is the relaxation factor multiplied with distance of estimate from observed image. [in] algo takes value of type enum af_iterative_deconv_algo indicating the iterative deconvolution algorithm to be used
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Returns
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sharp image estimate generated from the blurred input
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Note
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relax_factor argument is ignore when it AF_ITERATIVE_DECONV_RICHARDSONLUCY algorithm is used.
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