• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.571-579
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• 1999

In this paper, a Hadamard transform imaging spectrometer(HTIS) is proposed by using a grill spectrometer. And we reconfigure the system by using the grill sectrometer which uses a left cyclic S-matrix instead of the conventional right cyclic one. Then, we model the Hadamard transform imaging spectrometer and apply the mask characteristics compensation method, i.e. $ {T}^{-1}$ method, to complete fast algorithm. Also, through computer simulations the superiority of the proposed system in this paper to the conventional Hadamard transform spectrometer(HTS) is proved and the performance of the two systems are compared by introducing average mean square error(AMSE) as the algebraic criterion.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.12
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• pp.1527-1536
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• 1999

In this paper, a triply-encoded Hadamard transform imaging spectrometer is proposed by applying the grill spectrometer to the Hadamard transform imaging spectrometer. The proposed system encodes the input radiation triply ; once through the input image mask and twice through the two masks in the grill spectrometer. We use an electro-optical mask in the grill spectrometer which is controlled by a left-cyclic simplex matrix. Then we modeled the system using $D^{-1}$ method. In this paper, the average mean square error associated with a recovered estimate is considered for performance evaluation. The relative performance is compared with those of the other conventional systems.

• Proceedings of the KIEE Conference
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• 1999.11c
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• pp.597-599
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• 1999

Triply encoded HTIS(hadamard transform imaging spectrometer) is a system which applies the grill spectrometer to the HTIS. we consider a nonideality of mask transparent characteristic in estimating spectrum. Triply encoded system increases the SNR(signal to noise ration) by multiplexing effect. In this paper, we suggest an advanced $T^{-1}$ method for spectrum recovery. Then, we proved the superiority of the suggested method by comparing the average MSE(mean square error) of the other recovery methods.