## 4 Experimental Results
### Comparison with State-of-the-Art Methods  
To the best of our knowledge, only the method proposed by [PERSON] et al. in References [13; 14] considers the problem of multi-color components correlation. Thus, we mainly compare our proposed method with this method, named [PERSON]'s C_LUT. For [PERSON]'s C_LUT, the correlation of multi-color components is considered, and the best linear estimator is employed. Moreover, the method based on wavelet in reference [24] is also compared with the proposed method. The method in reference [24] is an improved method based on the method proposed by [PERSON] [6]. In the experiment, the PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index Measurement) are used to evaluate the performance of restored continuous color images. The experimental results are listed in Table 3, which shows the PSNR and SSIM of 10 classical images restored by the proposed method and other methods. In Table 3, our method M_LUT_ELM achieves the highest value for all of the 10 images. To further remove the noise in the smooth areas of the restored image, the average filter with a radius of 3 is used. Compared to the method of M_LUT_ELM in Table 3, the performance of all but one of the 10 images has improved on PSNR and SSIM by the method of M_LUT_ELM + filter. Especially, the average PSNR and SSIM obtain the best performance compared with the other methods.  
Figure 5: Comparison of the relative error of the estimated values using two ELM methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Wavelet Method**[24] & **Mese’s C\_LUT** & **M\_LUT\_ELM** & **M\_LUT\_ELM+Filter** \\ \hline Airplane & 25.47/0.68 & 26.16/0.71 & 26.40/0.72 & **27.75/0.84** \\ Anhinga & 23.15/0.62 & 25.21/0.65 & 25.38/0.65 & **25.57/0.72** \\ Baboon & 21.30/0.59 & 22.04/0.62 & **22.26/0.72** & 21.93/0.57 \\ Bardowl & 24.35/0.65 & 25.61/0.69 & 25.76/0.70 & **26.16/0.71** \\ Bluheron & 26.12/0.64 & 28.23/0.65 & 28.28/0.65 & **29.67/0.76** \\ Bobcat & 26.23/0.62 & 27.35/0.67 & 27.45/0.67 & **28.29/0.74** \\ Lena & 25.87/0.60 & 27.32/0.61 & 27.92/0.64 & **28.75/0.76** \\ Parrot & 25.64/0.52 & 27.32/0.46 & 27.98/0.50 & **28.63/0.60** \\ Peppers & 24.38/0.51 & 25.18/0.54 & 25.82/0.59 & **27.28/0.70** \\ Sailboat & 23.89/0.56 & 25.22/0.65 & 25.44/0.66 & **26.11/0.72** \\ \hline
**Average** & 24.64/0.60 & 25.96/0.63 & 26.27/0.64 & **27.01/0.71** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison of the proposed method with other methods (PSNR/SSIM).  
Besides the objective evaluation, we applied high-definition color images to do subjective evaluation and comparison. Figure 6 gives some reconstructed continuous-tone images by using the proposed method and the method [PERSON]'s C_LUT. From Figure 6, we can see that the visual effect of the recovered continuous-tone images by our method is higher than that of the method [PERSON]'s C_LUT, in which more fine details are presented, such as [PERSON]'s beard, [PERSON]'s feather on the head, and [PERSON]'s brim et al. In addition, the enlarged view of [PERSON]'s brim is given in Figure 6c, we can see that more details are recovered, and the number of noise points is reduced in flat areas by our method.