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The result can be seen in Fig. 5. In Fig. 1, and in Fig. 0. Note that the response curve became smoother. The second experiment is with the Robertson, Borman, and Stevenson algorithm, where we changed the variance of the weighing function. The result can be seen in Fig. 6. In Fig. 6(a) we set the parameter σ = 16, in Fig. 6(b) we set the parameter σ = 2, and in Fig. 6(c) we set the parameter σ = 40. Note that the response curve achieves the best regularity with σ = 16. 5: Response curves reconstructed with the Debevec and Malik algorithm with different values of the smoothness parameter.

The second experiment is with the Robertson, Borman, and Stevenson algorithm, where we changed the variance of the weighing function. The result can be seen in Fig. 6. In Fig. 6(a) we set the parameter σ = 16, in Fig. 6(b) we set the parameter σ = 2, and in Fig. 6(c) we set the parameter σ = 40. Note that the response curve achieves the best regularity with σ = 16. 5: Response curves reconstructed with the Debevec and Malik algorithm with different values of the smoothness parameter. 6: Response curves reconstructed with the Robertson, Borman, and Stevenson algorithm with different values of the weighting function.

2) In order to reconstruct the function g and the irradiances Ei j , we will need to minimize the function: g (zi j,k ) − ln wi j − ln tk O= (i j )k 2 . 3) book Mobk090 26 January 7, 2008 23:37 HIGH DYNAMIC RANGE IMAGE RECONSTRUCTION As we have a large number of equations and O is quadratic in the Ei j and g (z), minimizing it is a linear least-squares problem. This type of minimization can be successfully solved using singular value decomposition (SVD) or QR factorization. In the paper, they solved it using SVD with very good results.

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