# Non-Local Means Hole Repair Algorithm Based on Adaptive Block
## 4 Conclusions  
The effectiveness of the proposed adaptive block-based deep image hole repair algorithm is successfully demonstrated in this study. Initially, the algorithm based on NLM for \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Image of Scenes** & **ndisp \({}^{*}\)** & \multicolumn{2}{c}{**NLM**} & \multicolumn{2}{c}{**MECI**} & \multicolumn{2}{c}{**AB-NLM**} \\ \hline  & & **DE** & **ALME** & **DE** & **ALME** & **DE** & **ALME** \\ \hline Art & 256 & 2.75 & 2.29 & 2.31 & 1.98 & 2.08 & 1.67 \\ \hline Teddy & 256 & 2.68 & 2.61 & 2.54 & 2.35 & 2.41 & 2.28 \\ \hline Moebius & 256 & 2.88 & 2.59 & 2.69 & 2.55 & 2.21 & 2.11 \\ \hline Recycle & 260 & 3.04 & 2.81 & 2.75 & 2.58 & 2.19 & 1.78 \\ \hline Jadeplant & 640 & 10.70 & 10.31 & 9.11 & 8.43 & 8.31 & 7.92 \\ \hline \hline \end{tabular}  
* The ‘ndisp’ commonly refers to the number of potential disparity values in the disparity map, representing the levels of disparity corresponding to depth differences.  
\end{table} 
Table 1: Comparative analysis of DE and ALME for NLM, MECI, and AB-NLM algorithms based on the Middlebury dataset. Figure 8: Display a comparative analysis of SSIM results among the MECI, NLM, and AB-NLM algorithms based on the Middlebury dataset. hole repair of void regions is expounded upon. Subsequently, an intelligent block factor is introduced upon the foundation of the NLM hole repair algorithm. This factor facilitates automatic adjustments of search and repair block sizes to accommodate void regions of various dimensions, thereby mitigating the complexities associated with parameter tuning. Moreover, optimal search and repair blocks are computed for each void region, aiding in their proficient restoration. Finally, a series of experiments is conducted utilizing the Middlebury stereo matching dataset and scenes captured by the Orbbec Astra S depth camera. The NLM, AB-NLM, and MECI algorithms proposed in this paper are subjected to both qualitative and quantitative assessments employing the RMSE, SSIM, PSNR, DE, and ALME metrics. The outcomes validate that the proposed algorithm consistently yields more authentic object contours in the majority of scenarios. Furthermore, its performance in void region restoration within in-depth images is notably superior. Our future work will focus primarily on the following research areas:  
1. Through an extensive series of experimental investigations, it has been ascertained that the algorithm proposed within the context of this study exhibits certain deficiencies in the realm of transparent object depth restoration. Consequently, a prospective avenue for further inquiry entails the formulation of algorithms capable of addressing the task of transparent object depth restoration. 2. As the proportion of missing regions in the image gradually increases, the restoration effectiveness of various methods, including the algorithm proposed in this paper, will decrease. This is manifested by varying degrees of distortion at the restoration locations. Regardless of whether based on traditional algorithms or deep learning-based approaches, the fundamental principle of image restoration involves filling in the missing regions using known information in a certain manner. Naturally, better restoration results are achieved when the unknown regions are minimized. However, when the proportion of masked areas becomes excessively large, the limited amount of known information cannot adequately support the predictive function of the restoration algorithm. Therefore, in the future, the incorporation of the concept of style transfer will be explored. Particularly for large-scale restoration, combining image-style prior information with the idea of style transfer will be considered. Two models will synchronize output features and mutually supervise each other, thereby enhancing the model's capability to restore large-scale missing regions. Conceptualization, L.L. ; methodology, B.Z. ; software, B.Z. ; data curation, B.Z. ; writing--original draft preparation, B.Z. ; writing--review and editing, B.Z. and L.L.