### Table 2 Unscented Kalman fllter.

"... In PAGE 7: ... For comparison, the EKF guarantees only p = 1. The UKF is presented in Table2 . In the predictor phase, the sigma-points... In PAGE 20: ...tates (L gt; 1) is straightforward (see, e.g., [19,21]). Within the time interval [ti ti+1] we follow the notation of Table2 but avoid the use of indexes i and i + 1. Let x be a Gaussian random variable at the beginning of the time step, fea- turing mean ^ x and covariance P .... ..."

### Table. 4.1: Results of the de-noising experiment. All test images have been corrupted by additive Gaussian noise with three different noise levels (28.1 dB, 24.6 dB, 22.2 dB). Four different de-noising techniques have been applied: NID: nonlinear isotropic diffusion filter; NAD: nonlinear anisotropic diffusion filter; W: local adaptive Wiener filter; EPW: edge preserving Wiener filter); fingerp.: fingerprint; moons.: moon-surface .

### Table 2: Image enhancement measures obtained by the 5 denoising methods tested on the kidney- ultrasound image. The S/MSE is given in dB. Values of the correlation measure, fl, close to unity denote optimal edge preservation performance.

2001

"... In PAGE 15: ...The correlation measure, fl should be close to unity for an optimal efiect of edge preservation. The obtained values of MSE, S/MSE, and fl for all methods applied to the kidney image are given in Table2 .... ..."

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### Table 2 shows the mean square error obtained for the three cubic lters and for the rational lter as reported in [8]. The rational lter had shown to lead to a lower mean square error than the lters presented in [1, 5, 7] for edge preserving noise smoothing.

1998

"... In PAGE 3: ... Table2 : Mean square error for the application of polynomial lters three times on the image \air eld quot; with SNR 6dB... ..."

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### Table 2 shows the mean square error obtained for the three cubic lters and for the rational lter as reported in [8]. The rational lter had shown to lead to a lower mean square error than the lters presented in [1, 5, 7] for edge preserving noise smoothing.

1998

"... In PAGE 3: ... Table2 : Mean square error for the application of polynomial lters three times on the image \air eld quot; with SNR 6dB... ..."

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### Table 1 Kalman filtering algorithm

2005

"... In PAGE 13: ... 6 (a), (b), (c) and (d), respectively. Next, we applied the proposed Kalman filter given in Table1 to exponential samples and obtain the predicted and smoothed states. In Fig.... ..."

### Table 4: Kalman Filter Results

2002

"... In PAGE 6: ...Table 4: Kalman Filter Results We have used 3 special FUs in the kalman update function and 2 special FUs in the predict state function, the perfor- mance is better in the case of latter because there were more operations that could be mapped to these special FUs than the former case. As can be observed from Table4 , the num- ber of cycles have come down to less than half in the pre- dict state function, which implies a fairly large performance gain. 6.... ..."

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### Table 4: Kalman Filter Results

2002

"... In PAGE 6: ...state function, the perfor- mance is better in the case of latter because there were more operations that could be mapped to these special FUs than the former case. As can be observed from Table4 , the num- ber of .cycles have come down to less than half in the pre- dict .... ..."

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### Table 3: Kalman Filter AFUs

2002

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### Table 4: Kalman Filter Results

2002

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