## 3 Results and Discussion
### The Comparison of Lp_HOGS with Lp_LOGS
#### 3.1.3 The Motion Blur
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Noise Level (\%)**} & \multicolumn{4}{c}{**Lp\_LOGS**} & \multicolumn{4}{c}{**Lp\_HOGS**} \\ \cline{3-8}  & & **PSNR (dB)** & **SSIM** & **GMSD** & **PSNR (dB)** & **SSIM** & **GMSD** \\ \hline \multirow{3}{*}{Passerby} & 30 & 41.4154 & 0.9473 & 0.0224 & **42.7863** & **0.9643** & **0.0109** \\  & 40 & 40.0856 & 0.9324 & 0.0384 & **41.4734** & **0.9594** & **0.0176** \\  & 50 & 38.0234 & 0.9293 & 0.0487 & **40.0456** & **0.9535** & **0.0311** \\ \hline \multirow{3}{*}{Station} & 30 & 41.0730 & 0.9534 & 0.0134 & **42.2171** & **0.9660** & **0.0048** \\  & 40 & 39.3011 & 0.9415 & 0.0253 & **40.7997** & **0.9629** & **0.0088** \\  & 50 & 36.5096 & 0.9263 & 0.0349 & **38.5807** & **0.9573** & **0.0139** \\ \hline \multirow{3}{*}{Truck} & 30 & 41.1586 & 0.9441 & 0.0132 & **42.1004** & **0.9665** & **0.0039** \\  & 40 & 39.1459 & 0.9408 & 0.0178 & **41.0177** & **0.9639** & **0.0058** \\  & 50 & 36.0788 & 0.9572 & 0.0291 & **39.1823** & **0.9590** & **0.0117** \\ \hline \multirow{3}{*}{Garden} & 30 & 43.1018 & 0.9877 & 0.0116 & **44.4994** & **0.9929** & **0.0063** \\  & 40 & 41.6052 & 0.9857 & 0.0144 & **43.3407** & **0.9913** & **0.0096** \\  & 50 & 38.3654 & 0.9733 & 0.0356 & **40.5691** & **0.9876** & **0.0269** \\ \hline \multirow{3}{*}{Stairs} & 30 & 43.2679 & 0.9900 & 0.0133 & **44.7027** & **0.9947** & **0.0102** \\  & 40 & 41.3666 & 0.9861 & 0.0221 & **42.9469** & **0.9939** & **0.0119** \\  & 50 & 38.2970 & 0.9779 & 0.0330 & **40.0135** & **0.9884** & **0.0343** \\ \hline \multirow{3}{*}{Corridor} & 30 & 41.8418 & 0.9888 & 0.0138 & **43.5307** & **0.9939** & **0.0092** \\  & 40 & 40.0657 & 0.9841 & 0.0281 & **41.8881** & **0.9920** & **0.0132** \\  & 50 & 37.3735 & 0.9703 & 0.0404 & **39.4419** & **0.9854** & **0.0368** \\ \hline \multirow{3}{*}{Car} & 30 & 41.1081 & 0.9562 & 0.0084 & **42.2795** & **0.9664** & **0.0048** \\  & 40 & 38.9419 & 0.9439 & 0.0155 & **40.9768** & **0.9635** & **0.0074** \\  & 50 & 35.9764 & 0.9350 & 0.0406 & **38.7575** & **0.9583** & **0.0157** \\ \hline \multirow{3}{*}{Buildings} & 30 & 39.8062 & 0.9577 & 0.0203 & 41.3484 & **0.9716** & **0.0080** \\  & 40 & 37.7007 & 0.9430 & 0.0359 & **39.9433** & **0.9673** & **0.0137** \\  & 50 & 34.1941 & 0.9151 & 0.0686 & **37.6026** & **0.9592** & **0.0243** \\ \hline \multirow{3}{*}{Zebra crossing} & 30 & 41.8608 & 0.9909 & 0.0110 & **43.6546** & **0.9952** & **0.0061** \\  & 40 & 40.2476 & 0.9880 & 0.0167 & **42.0393** & **0.9934** & **0.0135** \\ \cline{1-1}  & 50 & 37.2561 & 0.9760 & 0.0414 & **39.2335** & **0.9891** & **0.0300** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Lp_HOGS and Lp_LOGS deblurring effects on images with the 7 \(\times\) 7 motion blur. The optimal indicators under each condition are denoted in bold. Figure 7: A comparison of deblurring effects of the two models on a truck image. **(a)** The original truck image, **(b)** the image deblurred using Lp_LOGS, and **(c)** the image deblurred using Lp_HOGS, **(d–f)** The local enlarged images of the red boxes from **(a–c)**, respectively.