# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 4 Experimental Results
### Quantitative Comparisons
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multicolumn{5}{c}{**(d) \(\delta=40\)**} \\ \cline{2-6}  & **NLM** & **KSVD** & **BM3D** & **EPLL** & **SC-SR** \\ \hline \multirow{3}{*}{**Hill**} & 24.71 & 27.15 & 27.93 & 27.76 & **28.05** \\  & 0.5766 & 0.6562 & **0.7053** & 0.6963 & 0.7039 \\  & 0.2446 & 0.4413 & 0.5207 & **0.5629** & 0.5388 \\ \hline \multirow{3}{*}{**Woman**} & 25.01 & 27.61 & 28.31 & 28.08 & **28.38** \\  & 0.6722 & 0.7258 & 0.7501 & 0.7383 & **0.7556** \\  & 0.3546 & 0.5039 & 0.5578 & **0.6506** & 0.5694 \\ \hline \multirow{3}{*}{**Average**} & 23.70 & 26.70 & 27.27 & 27.06 & **27.35** \\  & 0.6493 & 0.7504 & 0.7809 & 0.7735 & **0.7851** \\  & 0.4382 & 0.5902 & 0.6287 & **0.6541** & 0.6393 \\ \hline \multirow{3}{*}{**Images**} & \multicolumn{5}{c}{**(e) \(\delta=60\)**} \\ \cline{2-6}  & **NLM** & **KSVD** & **BM3D** & **EPLL** & **SC-SR** \\ \hline \multirow{3}{*}{**Baboon**} & 20.95 & 22.08 & 22.38 & 22.48 & **22.56** \\  & 0.3568 & 0.4356 & 0.4685 & 0.4890 & **0.4901** \\  & 0.2680 & 0.3077 & 0.3173 & 0.4114 & **0.4358** \\ \hline \multirow{3}{*}{**Fingerprint**} & 18.95 & 21.77 & 23.59 & 22.65 & **23.63** \\  & 0.4952 & 0.6804 & **0.7974** & 0.7597 & 0.7900 \\  & 0.5073 & 0.4908 & 0.5027 & 0.4514 & **0.5731** \\ \hline \multirow{3}{*}{**Airplane**} & 23.27 & 26.01 & 27.01 & 26.95 & **27.13** \\  & 0.7273 & 0.7602 & 0.7860 & 0.7767 & **0.8163** \\  & 0.3625 & 0.4633 & 0.5389 & **0.6253** & 0.5881 \\ \hline \multirow{3}{*}{**Monarch**} & 20.43 & 24.22 & 24.58 & 24.72 & **24.81** \\  & 0.6503 & 0.7618 & 0.7777 & 0.7795 & **0.8006** \\  & 0.5406 & 0.7315 & 0.7300 & **0.7614** & 0.7472 \\ \hline \multirow{3}{*}{**Lena**} & 24.59 & 26.90 & 27.98 & 27.60 & **28.05** \\  & 0.6999 & 0.7328 & 0.7636 & 0.7465 & **0.7871** \\  & 0.2777 & 0.3298 & 0.4394 & **0.5013** & 0.4753 \\ \hline \multirow{3}{*}{**House**} & 23.59 & 26.75 & 28.49 & 27.90 & **28.53** \\  & 0.6967 & 0.7252 & **0.7768** & 0.7636 & 0.7669 \\  & 0.3941 & 0.4638 & 0.6288 & **0.6379** & 0.6204 \\ \hline \multirow{3}{*}{**Peppers**} & 21.15 & 25.02 & 25.51 & **25.67** & 25.45 \\  & 0.6634 & 0.7386 & 0.7479 & 0.7621 & **0.7717** \\  & 0.3990 & 0.5282 & 0.5401 & **0.6354** & 0.5801 \\ \hline \multirow{3}{*}{**Straw**} & 18.59 & 20.51 & 21.26 & 20.99 & **21.39** \\  & 0.2785 & 0.4726 & 0.5824 & 0.5521 & **0.5970** \\  & 0.4419 & 0.4592 & 0.4804 & 0.4240 & **0.4837** \\ \hline \multirow{3}{*}{**Hill**} & 23.54 & 25.61 & **26.35** & 26.20 & 26.29 \\  & 0.5283 & 0.5928 & 0.6332 & 0.6262 & **0.6355** \\  & 0.2153 & 0.2665 & 0.4002 & **0.4424** & 0.4223 \\ \hline \multirow{3}{*}{**Woman**} & 23.47 & 25.86 & **26.55** & 26.43 & 26.49 \\  & 0.6251 & 0.6701 & 0.6948 & 0.6810 & **0.7063** \\  & 0.3299 & 0.3405 & 0.4232 & **0.5175** & 0.4604 \\ \hline \multirow{3}{*}{**Average**} & 21.85 & 24.47 & 25.37 & 25.16 & **25.43** \\  & 0.5722 & 0.6570 & 0.7028 & 0.6936 & **0.7162** \\  & 0.3736 & 0.4381 & 0.5001 & **0.5408** & 0.5386 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._From Table 1, we could observe three points. Firstly, the proposed SC-SR algorithm achieved much better PSNR, SSIM and FOM results than NLM and K-SVD in all cases. Secondly, SC-SR had higher PSNR and SSIM values than EPLL in most cases. Moreover, EPLL acquired the best FOM results among the five algorithms, and SC-SR was only slightly inferior to EPLL. Thirdly, SC-SR obtained better SSIM and FOM results than BM3D in most cases. Meanwhile, when the noise variance was low, the PSNR results of SC-SR were close to BM3D; when the noise variance was high, the PSNR results of SC-SR were obviously better than BM3D, since BM3D trended to suffer from artifacts in this case. According to the these points, we can come to the conclusion that SC-SR is capable of stronger comprehensive ability in reservation of structural, edge and grayscale information and does better in denoising images in comparison with the other algorithms.