# Multi-Color Channels Based Group Sparse Model for Image Restoration
## 4 Experimental Results
### Objective Assessment
It can be concluded from Tables 1 and 2 that the PSNR of the proposed model in this paper on the images with 80%, 70%, 60%, and 50% pixel missing rates are higher than the nine state-of-the-art comparison methods. Table 3 provides the comparison results of the proposed model with deep-learning-based IR-CNN and ISBP models. In terms of average metric values, the PSNR and SSIM values of the proposed model are 4.1 dB and 0.049 higher than those of the IR-CNN model, and 4.72 dB and 0.067 higher than those of the IDBP model, respectively. It can be seen from the PSNR and SSIM values in Table 3 that the proposed model based on multi-color channels is better than the IR-CNN and IDBP models. The experimental results in Tables 1-3 show that the model proposed in this paper is effective and can obtain good restoration results compared with the state-of-the-art models. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multicolumn{2}{c}{**Pixel Missing Rate 80\%**} & \multicolumn{2}{c}{**Pixel Missing Rate 70\%**} & \multicolumn{2}{c}{**Pixel Missing Rate 60\%**} & \multicolumn{2}{c}{**Pixel Missing Rate 50\%**} \\ \cline{2-11}  & **IR-CNN** & **IDBP** & **Proposed** & **IR-CNN** & **IDBP** & **Proposed** & **IR-CNN** & **IDBP** & **Proposed** & **IR-CNN** & **IDBP** & **Proposed** \\ \hline \multirow{2}{*}{Cowboy} & 25.47 & 24.43 & **29.38** & 27.54 & 27.15 & **32.31** & 29.95 & 28.97 & **35.36** & 31.96 & 31.40 & **38.12** \\  & 0.8622 & 0.8400 & **0.9343** & 0.9163 & 0.8955 & **0.9621** & 0.9479 & 0.9273 & **0.9770** & 0.9660 & 0.9522 & **0.9854** \\ \hline \multirow{2}{*}{Girl} & 25.36 & 25.03 & **29.28** & 27.78 & 27.45 & **32.26** & 30.10 & 29.32 & **34.70** & 32.10 & 31.09 & **37.15** \\  & 0.8135 & 0.7999 & **0.9212** & 0.8910 & 0.8698 & **0.9549** & 0.9327 & 0.9102 & **0.9720** & 0.9564 & 0.9371 & **0.9823** \\ \hline \multirow{2}{*}{Flower} & 28.41 & 28.12 & **32.14** & 30.77 & 30.49 & **35.11** & 32.97 & 32.16 & **37.59** & 35.28 & 34.15 & **40.00** \\  & 0.8586 & 0.8413 & **0.9381** & 0.9140 & 0.8943 & **0.9652** & 0.9460 & 0.9264 & **0.9786** & 0.9661 & 0.9508 & **0.9864** \\ \hline \multirow{2}{*}{Lake} & 25.24 & 25.39 & **28.86** & 27.59 & 27.87 & **31.33** & 29.75 & 29.53 & **33.76** & 31.80 & 31.47 & **35.84** \\  & 0.8278 & 0.8203 & **0.9115** & 0.8914 & 0.8805 & **0.9433** & 0.9302 & 0.9155 & **0.9623** & 0.9535 & 0.9421 & **0.9743** \\ \hline \multirow{2}{*}{Mickey} & 26.45 & 25.40 & **29.57** & 29.66 & 28.69 & **32.24** & 31.82 & 31.18 & **34.51** & 34.25 & 33.14 & **36.62** \\  & 0.8671 & 0.8436 & **0.9260** & 0.9220 & 0.9023 & **0.9532** & 0.9474 & 0.9319 & **0.9687** & 0.9637 & 0.9517 & **0.9787** \\ \hline \multirow{2}{*}{Mural} & 25.75 & 25.26 & **29.67** & 28.73 & 27.68 & **32.30** & 30.58 & 29.79 & **34.70** & 32.20 & 31.35 & **37.10** \\  & 0.7923 & 0.7694 & **0.8960** & 0.8721 & 0.8388 & **0.9393** & 0.9091 & 0.8824 & **0.9633** & 0.9361 & 0.9148 & **0.9780** \\ \hline \multirow{2}{*}{Average} & 26.11 & 25.61 & **29.82** & 28.68 & 28.22 & **32.59** & 30.86 & 30.16 & **35.10** & 32.93 & 32.10 & **37.47** \\  & 0.8369 & 0.8191 & **0.9211** & 0.9011 & 0.8802 & **0.9530** & 0.9356 & 0.9156 & **0.9703** & 0.9570 & 0.9415 & **0.9808** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: PSNR and SSIM values of the proposed method and two deep-learning-based methods with four different pixel missing rates.