# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 4 Experimental Results
### Quantitative Comparisons  
To further validate the denoising capability of the proposed algorithm, we selected PSNR, SSIM and FOM as indexes to quantitatively evaluate the performance of the SC-SR algorithm. PSNR is one of the most widely used image objective evaluation indexes, and is able to measure the similarity of grayscale information between the original image and the denoised image. Since PSNR is based only on the error between the corresponding pixels, it cannot comprehensively describe structural similarity and the degree of edge preservation. SSIM is capable of assessing structural similarity, and FOM can be used to measure the degree of edge preservation between the original image and the denoised image. Table 1 presents the PSNR, SSIM and FOM results for different algorithms, images, and noise variances. As presented in Table 1, the top value is the PSNR result, the middle value is the SSIM result, and the bottom value is the FOM result in every table cell. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multicolumn{5}{c}{**(a) \(\boldsymbol{\delta}=\boldsymbol{5}\)**} \\ \cline{2-6}  & **NLM** & **KSVD** & **BM3D** & **EPLL** & **SC-SR** \\ \hline \multirow{3}{*}{**Baboon**} & 34.48 & 35.44 & **35.49** & **35.49** & **35.49** \\  & 0.9288 & 0.9536 & 0.9534 & **0.9552** & 0.9519 \\  & 0.9123 & 0.9326 & 0.9297 & 0.9332 & **0.9351** \\ \hline \multirow{3}{*}{**Fingerprint**} & 34.44 & 36.63 & 36.51 & 36.43 & **36.65** \\  & 0.9807 & 0.9878 & 0.9876 & 0.9875 & **0.9887** \\  & 0.9056 & 0.8919 & **0.9876** & 0.9038 & 0.9642 \\ \hline \multirow{3}{*}{**Airplane**} & 37.40 & 39.07 & 39.25 & 39.21 & **39.31** \\  & 0.9425 & 0.9584 & 0.9595 & **0.9604** & 0.9598 \\  & 0.9278 & 0.9251 & 0.9331 & 0.9372 & **0.9388** \\ \hline \multirow{3}{*}{**Monarch**} & 36.72 & 37.74 & 38.25 & 38.27 & **38.29** \\  & 0.9677 & 0.9720 & 0.9756 & 0.9755 & **0.9758** \\  & 0.9789 & 0.9738 & 0.9762 & **0.9771** & 0.9751 \\ \hline \multirow{3}{*}{**Lena**} & 37.17 & 38.62 & 38.71 & 38.59 & **38.74** \\  & 0.9239 & 0.9455 & 0.9444 & 0.9449 & **0.9450** \\  & 0.9113 & 0.9125 & **0.9303** & 0.9281 & 0.9289 \\ \hline \multirow{3}{*}{**House**} & 37.34 & 39.43 & 39.86 & 38.97 & **39.94** \\  & 0.9164 & 0.9546 & 0.9568 & 0.9498 & **0.9584** \\  & 0.9383 & 0.9468 & **0.9535** & 0.9395 & 0.9442 \\ \hline \multirow{3}{*}{**Peppers**} & 36.70 & 37.81 & 38.10 & 37.98 & **38.15** \\  & 0.9409 & 0.9550 & 0.9558 & **0.9562** & 0.9551 \\  & 0.9361 & 0.9378 & 0.9407 & **0.9508** & 0.9506 \\ \hline \multirow{3}{*}{**Straw**} & 34.40 & 35.49 & 35.43 & 35.36 & **35.52** \\  & 0.9813 & 0.9850 & 0.9848 & 0.9846 & **0.9864** \\  & 0.9200 & 0.9218 & 0.9198 & 0.9143 & **0252** \\ \hline \multirow{3}{*}{**Hill**} & 35.53 & 37.00 & 37.13 & 37.03 & **37.18** \\  & 0.9085 & 0.9423 & 0.9427 & **0.9437** & 0.9431 \\  & 0.8740 & 0.9181 & **0.9266** & 0.9216 & 0.9256 \\ \hline \multirow{3}{*}{**Woman**} & 36.06 & 37.26 & **37.45** & 37.33 & 37.42 \\  & 0.9056 & 0.9336 & 0.9325 & **0.9347** & 0.9329 \\  & 0.9104 & 0.9291 & **0.9343** & 0.9306 & 0.9331 \\ \hline \multirow{3}{*}{**Average**} & 36.024 & 37.449 & 37.62 & 37.47 & **37.67** \\  & 0.9396 & 0.95878 & 0.9593 & 0.9593 & **0.9597** \\ \cline{1-1}  & 0.9215 & 0.92895 & **0.9432** & 0.9336 & 0.9421 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The PSNR, SSIM and FOM results for different denoising algorithms. Best results are in bold.