# Role of Restored Underwater Images in Underwater Imaging Applications
## 5 Conclusions  
A proposed restoration method for underwater images is given in this paper, which involves depth estimation from blurriness estimation and background light neutralization process as well a transmission map estimation using direct and backscattered signals. Any method that is designed for restoring the image has to provide efficient results not only in terms of quantitative and qualitative approach, but also in practical applications, which serves the whole purpose of developing the method in the first place. For this reason, the proposed underwater image restoration method has been tested on direct underwater applications. The applications are chosen in such a way that the claims of the proposed methods are proven. Edge detection, SURF and image classification have been chosen. The edge detection has been performed using the Sobel edge-detection operator on raw and restored images using the proposed method, and it has been shown that the number of edges detected on the restored images are always higher than on the original raw images, proving that the proposed restoration method is able to reduce blurriness in an image. Similarly, the SURF function from MATLAB has been used on the gray version of raw and restored underwater images. Subsequently, it has been shown that the number of SURF points detected on the restored underwater images increases. This implies the ability of the proposed method to improve texture details of an underwater image. Finally, image classification using a supervised machine-learning approach has been performed on a set of 75 test images before and after restoration, whereby the restored images give better classification results in terms of accuracy when used for image classification using both SVM and KNN.  
These results on different underwater imaging applications show that the recovered images from the proposed method provide good and efficient results when compared to unprocessed raw input images. As such, the proposed method can be considered as an important step to recover underwater images before applying them to underwater applications, with an efficient outcome.  
Conceptualization, J.R.A. and P.E.A.; methodology, J.R.A. and P.E.A.; software, J.R.A.; validation, J.R.A., P.E.A. and L.C.D.S.; formal analysis, J.R.A.; investigation, J.R.A.; resources, P.E.A.; data curation, J.R.A.; writing--original draft preparation, J.R.A.; writing--review and editing, J.R.A. and P.E.A.; visualization, J.R.A.; supervision, P.E.A. and L.C.D.S.; project administration, P.E.A. and L.C.D.S.; funding acquisition, P.E.A. and L.C.D.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported in part by UBD Faculty Research Grant (UBD/RSCH/1.3/FICBF(b)/2018/001).  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Unprocessed Input Images**} & \multicolumn{2}{c}{**Restored Output Images**} \\ \cline{2-5}  & **SVM** & **KNN** & **SVM** & **KNN** \\ \hline Sensitivity (\%) & 73 & 69 & 85 & 79 \\ Specificity (\%) & 90 & 77 & 91 & 84 \\ Accuracy (\%) & 72 & 82 & 83 & 89 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tabulation of SVM and KNN feature values on unprocessed and restored underwater images.