# Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
## 5 Experimental Results and Analysis
### Experiments on Real Data  
The proposed algorithm is tested on real data which comes from [17], except for our own synthetic data. Each group of images in the real dataset has 31 low resolution images with various motions and blurring in the same scene. The quality of real images cannot be evaluated by image evaluation criteria such as PSNR, SSIM and IFC. In order to test our method, we use no-reference assessment metrics, such as Natural Image Quality Evaluator (NIQE) [30], Perception-Based Image Quality Evaluator (PIQE) [31] and No-Reference Quality Metric (NRQM) [32] for super-resolved images. The results of NIQE, PIQE and NRQM on real data are presented in Table 2. Visual comparison results of the IMG_0663 image and the IMG_1480 image are shown in Figures 7 and 8. Figure 6: Visual comparison of algorithms for the Butterfly image with \(\sigma_{noise}=0.005\) when the magnification factor is 4. (**a**) The LR image; (**b**) Bicubic interpolation; (**c**) BayesSR; (**d**) SRB; (**e**) DeepSR; (**f**) FBSR; (**g**) Ours; (**h**) ground truth. We can see that the values of NIQE, PIQE and NRQM have been improved on real data from Table 2. The values of NIQE, PIQE and NRQM increase by 4.45%, 10.56% and 15.75%. It can be seen from Figure 7 that bicubic interpolation, BayesSR and DeepSR have different degrees of blur. SRB and FBSR have obvious ringing artifacts. Figures 7 and 8 show that the proposed algorithm can produce better results on real data. The estimated HR image can preserve the details of the image and has no obvious artifacts. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Images** & **Assessment Metrics** & **Bicubic Interpolation** & **BayesSR** & **SRB** & **DeepSR** & **FBSR** & **Ours** \\ \hline \multirow{3}{*}{IMG\_0659} & NIQE & 8.602 & 8.9946 & 8.0072 & 7.7975 & 6.8901 & **6.0805** \\  & PIQE & 90.0698 & 75.6895 & 66.9028 & 58.9616 & 43.2765 & **38.3211** \\  & NRQM & 3.9608 & 3.154 & 3.944 & 2.9465 & 4.0102 & **4.408** \\ \hline \multirow{3}{*}{IMG\_0663} & NIQE & 39.8007 & 39.8026 & 39.8 & 39.8005 & 39.7992 & **39.796** \\  & PIQE & 74.6022 & 65.3371 & 59.1997 & 77.0891 & 56.9802 & **49.5662** \\  & NRQM & 3.7656 & 4.4025 & 4.7599 & 4.2321 & 4.9025 & **5.5618** \\ \hline \multirow{3}{*}{IMG\_0667} & NIQE & 11.3632 & 9.3836 & 8.9293 & 8.2567 & 7.9208 & **7.2685** \\  & PIQE & 90.1674 & 66.6976 & 69.7264 & 77.3032 & 60.2489 & **58.4445** \\  & NRQM & 3.401 & 3.6383 & 3.0631 & 2.6799 & 3.8943 & **4.0978** \\ \hline \multirow{3}{*}{IMG\_0687} & NIQE & 10.4796 & 8.9724 & 8.3236 & 9.7315 & 8.7233 & **8.0351** \\  & PIQE & 90.9179 & 69.031 & 67.1065 & 53.1181 & 50.2467 & **42.425** \\  & NRQM & 2.8295 & 2.6297 & 2.9789 & 2.5811 & 3.0874 & **3.8819** \\ \hline \multirow{3}{*}{IMG\_1480} & NIQE & 8.4942 & 6.1062 & 6.0722 & 7.7383 & 6.0002 & **5.9913** \\  & PIQE & 80.8595 & 74.114 & 59.0136 & 72.4327 & 56.3024 & **54.7688** \\  & NRQM & 4.4241 & 4.5514 & 2.0487 & 2.8916 & 4.8912 & **5.4116** \\ \hline \multirow{3}{*}{IMG\_1481} & NIQE & 9.2407 & 8.7282 & 8.8947 & 6.7134 & **6.6962** & 6.7838 \\  & PIQE & 92.4789 & 74.5397 & 68.7935 & 88.9525 & 50.5608 & **41.6472** \\ \cline{1-1}  & NRQM & 4.4951 & 3.0268 & 2.5334 & 2.3857 & 5.4580 & **7.0695** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: NIQE, PIQE and NRQM results of our algorithms on real data when the magnification factor is 5. Figure 7: Visual comparison of algorithms for the IMG_0663 image when the magnification factor is 5. (**a**) The LR image; (**b**) Bicubic interpolation; (**c**) BayesSR; (**d**) SRB; (**e**) DeepSR; (**f**) FBSR; (**g**) Ours. It can be drawn from the experimental results on synthetic data and real data that our proposed method is superior to the previous multi-frame blind super-resolution methods. The reason for this is that we combine the parameters of image reconstruction, blur kernel estimation and motion estimation. The error of each estimation can be further reduced through iteration. On the one hand, this method can quickly correct the error of motion estimation and blur estimation.