# Hyperspectral Image Inpainting Based on Robust Spectral Dictionary Learning
## 5 Experimental Results and Analysis
### Real Data Experiment  
In this section, the algorithm was applied to the real HSI dataset Urban ([[http://www.agc.army.mil/](http://www.agc.army.mil/)]([http://www.agc.army.mil/](http://www.agc.army.mil/))). The Urban scene was acquired by the hyperspectral digital collection experiment (HyDICE) sensor during a flight over Copperas Cove, Texas. The dataset had a spatial size of \(307\times 307\) and included 210 bands ranging from 0.4 to 2.5 \(\upmu\)m, with a spectral resolution of 10 nm, as shown in Figure 6. Its spatial resolution was about \(2\times 2\) m. Due to the influence of water vapor and atmospheric effects, the image had strong noise in many bands, and these bands contained little useful information.  
To verify the effectiveness of the algorithm, the proposed algorithm was compared with other hyperspectral inpainting algorithms: PDE, UBD, LLRSSTV, and BPFA. In dictionary learning process, the regularization parameter \(\lambda\) of the proposed INORSDL is set to 1. Figures 7-9 display the inpainting results of the various hyperspectral repair algorithms in the 105 th, 144 th, and 208 th bands, respectively. It is evident in the figure that the INORSDL algorithm proposed in this paper exhibited better inpainting  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Index** & **Noisy Image** & **PDE** & **UBD** & **LLRSSTV** & **BPFA** & **INORSDL** \\ \hline \multirow{2}{*}{Gaussian} & MPSNR (dB) & 19.7997 & 20.0392 & 34.4112 & 30.8946 & 35.6697 & **36.7168** \\  & MSSIM & 0.4331 & 0.4435 & 0.9355 & 0.9120 & 0.9572 & **0.9687** \\  & Time (s) & - & 2 & 53 & 597 & 1126 & 903 \\ \hline \multirow{2}{*}{Gaussian} & MPSNR (dB) & 28.1013 & 28.6221 & 37.5838 & 34.1243 & 32.8583 & **38.9195** \\ non-i.i.d & MSSIM & 0.7057 & 0.7206 & 0.9686 & 0.9580 & 0.8240 & **0.9895** \\ noise & Time (s) & - & 2 & 60 & 574 & 5152 & 1621 \\ \hline \multirow{2}{*}{Laplacian} & MPSNR (dB) & 33.1715 & 33.9045 & 37.9209 & 35.3268 & 38.2032 & **38.5285** \\  & MSSIM & 0.9292 & 0.9457 & 0.9873 & 0.9728 & 0.9892 & **0.9914** \\ noise & Time (s) & - & 2 & 51 & 586 & 1525 & 1124 \\ \hline \multirow{2}{*}{Poisson noise} & MPSNR (dB) & 26.5466 & 26.9952 & 35.9059 & 33.8843 & 37.3994 & **40.6220** \\  & MSSIM & 0.7476 & 0.7609 & 0.9647 & 0.9599 & 0.9669 & **0.9870** \\  & Time (s) & - & 1 & 36 & 287 & 9779 & 1531 \\ \hline \hline \end{tabular} The highest value in each row uses the bold font.  
\end{table}
Table 1: Performance comparison of different hyperspectral inpainting algorithms applied to Pavia Centre dataset.  
Figure 6: 3D-cube of Urban hyperspectral data set.  
results than the other inpainting algorithms. However, although the LLRSSTV algorithm removed noise while restoring damaged pixels, the inpainted image showed noticeable patch effects, and some local restorations were unsatisfactory.  
Figure 8: Inpainting results of band 144 of Urban dataset. (**a**) Original image; (**b**) PDE; (**c**) UBD; (**d**) LLRSSTV; (**e**) BPFA; and (**f**) INORSDL.  
Figure 7: Inpainting results of band 105 of Urban dataset. (**a**) Original image; (**b**) PDE; (**c**) UBD; (**d**) LLRSSTV; (**e**) BPFA; and (**f**) INORSDL.