## 3 Experiments
### Tests on Real Ultrasound Images  
Considering that the synthetic images may not fully cover all of the features of real ultrasound imaging conditions, we also conducted tests on real ultrasound images. Generally speaking, the noise levels vary for different organs. Therefore, we selected four representative ultrasound images, captured at the locations of thyroid, breast, liver, and spleen to show the generalization ability of the proposed strategy. Figures 6 and 7 compare the processed results of the different methods. We also show zoomed-in patches for regions of interest (ROIs), which were curated by physicians for clinical diagnosis (i.e., the ROIs were picked by a physician in the department of ultrasound imaging), for each image tested here. For example, a tumor exists in the thyroid image (see the first row of Figure 6), which needs to be paid more attention to distinguish whether it is benign or not. We can see that the method of bilateral filtering can effectively reduce the speckle noise, but the processed images are obviously over-smoothed. In particular, considering that identifying calcification is very helpful for discriminating whether a tumor is benign or malignant, the over-smoothing of the images makes it difficult to identify this property of the tumor. In constrast, the images processed by the OBNLM method [29] exhibited stripe patterns, which made it unable to accurately show the real tissue situation. A possible reason for this is the inaccurate selection of similar patches in OBNLM, due to the high level of speckle noise. By contrast, our method and the method proposed by [PERSON] et al. [14] effectively reduced the speckle noise while retaining image details. Please note that our method preserved more details, in comparison to the method of [PERSON] et al. [14]. A similar phenomenon also happens for other \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Figure 5b**} & \multirow{2}{*}{**Figure 5d**} \\ \cline{2-2} \cline{4-6}  & \(\delta=0.15\) & \(\delta=0.2\) & \(\delta=0.25\) & \(\delta=0.3\) & \\ \hline Original & 23.35 & 22.26 & 21.41 & 21.07 & 17.73 \\ Bilateral filter & 26.32 & 24.88 & 23.73 & 23.31 & 18.77 \\ OBNLM [29] & 27.20 & 25.77 & 24.69 & 24.31 & 18.19 \\ [PERSON] et al. [14] & 27.10 & 26.23 & 25.56 & 25.24 & 18.64 \\ Proposed method & 26.84 & 25.59 & 24.52 & 24.13 & 19.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The peak signal-to-noise (PSNR) metric of various methods on the two synthetic images in Figure 5. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Figure 5b**} & \multirow{2}{*}{**Figure 5d**} \\ \cline{2-2} \cline{4-6}  & \(\delta=0.15\) & \(\delta=0.2\) & \(\delta=0.25\) & \(\delta=0.3\) & \\ \hline Original & 0.71 & 0.69 & 0.68 & 0.68 & 0.28 \\ Bilateral filter & 0.95 & 0.95 & 0.94 & 0.93 & 0.53 \\ OBNLM [29] & 0.94 & 0.92 & 0.91 & 0.89 & 0.39 \\ [PERSON] et al. [14] & 0.96 & 0.96 & 0.95 & 0.94 & 0.40 \\ Proposed method & 0.95 & 0.94 & 0.93 & 0.93 & 0.61 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The structural similarity (SSIM) metric of various methods on the two synthetic images in Figure 5.  
In particular, the technique of superpixel segmentation was used to sparsely code the input image, which can be regarded as a superpixel version of bilateral filtering. Then, we treated the reconstructed image from the sparse representation of the superpixel segmentation as the low-frequency component, and a HVS-inspired spatial compensation was designed to fine-tune the image details that were to be recovered. Experiments on multiple synthetic and real UIs showed that the proposed method performs better than the state-of-the-art methods in terms of visual observation and objective metric evaluation.