Abstract
Underwater images are extremely sensitive to distortion occurring in an aquatic underwater environment, with absorption, scattering, polarization, diffraction and low natural light penetration representing common problems caused by sea water. Because of these degradation of quality, effectiveness of the acquired images for underwater applications may be limited. An effective method of restoring underwater images has been demonstrated, by considering the wavelengths of red, blue, and green lights, attenuation and backscattering coefficients. The results from the underwater restoration method have been applied to various underwater applications; particularly, edge detection, Speeded Up Robust Feature detection, and image classification that uses machine learning. It has been shown that more edges and more SURF points can be detected as a result of using the method. Applying the method to restore underwater images in image classification tasks on underwater image datasets gives accuracy of up to 89% using a simple machine-learning algorithm. These results are significant as it demonstrates that the restoration method can be implemented on underwater system for various purposes.
Highlights
A considerable part of the earth is covered with water, with the underwater world consisting of an astounding variety of resources
The proposed underwater restoration method has been used to process numerous The proposed underwater restoration method has been used to process numerous underwater images, with the processed underwater images used as input for the three underwater images, with the processed underwater images used as input for the three underwater applications: edge detection, Speeded Up Robust Feature (SURF), and image classification applications
It can be seen that the proposed method is able to improve sensitivity, specificity, and accuracy compared to using the raw unprocessed underwater images
Summary
A considerable part of the earth is covered with water, with the underwater world consisting of an astounding variety of resources. Due to these problems, a proper method that is able to restore. To restore the Software-based approaches use to restore the capcaptured underwater images, by finding the background light and transmission maps. The proposed method involves the use of depth estimation strategy to restore the underwater images. The proposed method involves the use of and transmission map estimation with attenuation coefficient priors, by considering the depth estimation and transmission map estimation with attenuation coefficient priors, by backscattering effect, and has been proven to show superior results [18]. The efficiency of an image-processing algorithm is calculated by comparits output processed underwater images to other similar algorithms, and by using quality ing its output processed underwater to other Mean similarSquared algorithms, by using metrics, such as
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
