# Role of Restored Underwater Images in Underwater Imaging Applications
## 4 Results
### Results of the SURF Application  
For detecting SURF features in underwater images, gray versions of the raw underwater images and restored underwater images in Figure 3 have been used, with the SURF function in MATLAB used for this purpose. Results of SURF points detected in raw im  
Figure 4: Edge Detection on unprocessed and restored images using the proposed method, for images taken with (**a**) Naturally lit, (**b**) Bluish nature (**c**) Greenish nature (**d**) Artificial light source, and (**e**) Backscattered image.  
detected in all the restored gray versions of the underwater images is higher than the input gray version. This clearly suggests that the proposed method is able to improve underwater images for the purpose of detection of objects as well as extraction of point of interest in underwater applications, by improving texture details of the processed image as well as reducing blurriness. Comparison between the number of SURF feature points detected in the gray version of raw input and restored underwater images in Figure 3a is shown in Figure 5a. The raw underwater image shown in Figure 3a is a naturally lit image, with the front part receiving more light than the background region. Consequently, in both the raw and restored images in Figure 5a, more features are detected in the front part of the image when compared with the back part of the image. Less feature points are detected in the front part of the restored image of Figure 5a when compared with the raw image of Figure 5a. Additionally, SURF feature points in the coral regions are also detected from the restored underwater image.  
Figure 5b depicts the comparison between raw and restored underwater images of a shipw. Less SURF points are detected on the raw image of Figure 5b, with the detected features concentrated mainly on the front part of the ship. On the other hand, SURF feature points on the front part as well as the top part of the ship can be clearly seen from the restored image in Figure 5b. This points to the effectiveness of the proposed restoration method.  
Figure 5c shows comparison of SURF features of an image with greenish nature between the raw and restored underwater images. On the raw image of Figure 5c, the detection of feature points focuses only on the coral reefs and not on the fish, but features are detected on the coral reefs as well on some of the fish in the restored image of Figure 5c. Thus, the restored image performs better than the input raw image.  
Figure 5d shows an image with an artificial light source. Generally, images which are lit artificially would have more distortions due to the reflection of artificial light source on floating particles in water. The restored underwater image using the proposed method handles the artificial light source problems gracefully by reducing the effects of distortions, as seen in Figure 3d. Consequently, more SURF points are detected on the sand particles as well as on the fish in the gray version of the restored image of Figure 5d, when compared with the input raw image of Figure 5d.  
Finally, Figure 5e shows comparison of the raw input and restored underwater image of image affected by backscattered signal. Only four SURF features are detected on the upper part of the sea turtle of the raw image in Figure 5e. On the other hand, more SURF features are noticeably detected on the restored image of Figure 5e, since the restored image deals with backscattering effects effectively, by using proper priors for backscattering attenuation coefficients.