Author : Anjali Puri and Dr. Satish Todmal

The visibility of scene was compensated by the object-camera distance to recover the colors of the background and objects. Subsequently, by analyzing the physical property of the point spread function, we developed a simple but efficient low-pass filter to debut degraded underwater images. A wide variety of underwater images with different scenarios were used for the experiments. A new method for subsea pipeline corrosion estimation by using color information of corroded pipe. As precursor steps, an image restoration and enhancement algorithm are developed for degraded underwater images. The developed algorithm minimizes blurring effects and enhances color and contrast of the images. The enhanced colors in the imaging data help in corrosion estimation process. The image restoration and enhancement algorithm are tested on both experimentally collected as well as publicly available hazy underwater images. A reasonable accuracy is achieved in corrosion estimation that helped to distinguish between corroded and non-corroded surface areas of corroded pipes. The qualitative and quantitative analyses show promising results that encourage to integrate the proposed method into a robotic system that can be used for realtime underwater pipeline corrosion inspection activity. Underwater image degradation, surveys the state-of-the-art intelligence algorithms like deep learning methods in underwater image  harm and restoration, demonstrates the performance of underwater image harm and color restoration with different methods, introduces an underwater image color evaluation metric, and provides an overview of the major underwater image applications

Keyword: Corrosion detection, Image process, Deep Learning, DCNN