Reduction of Artifacts and Edge Preservation of Underwater Images Using Deep Convolution Neural Network

Abstract

Underwater exploration has been one of the areas of active research over the last few decades. Image enhancement is difficult for computer vision-based underwater research because of the degradation of the underwater environment’s images. Several types of artifact reduction approaches are already available of value averaging filters to smooth the continuity that artifact reduction approaches appear across image boundaries. While some of these approaches can somewhat reduce these unwanted artifacts’ severity, other approaches have some limitations that can cause blurring to high-contrast edges in the image. Therefore we need novel methods to overcome the theses’ drawbacks. This research work introduced a Deep Convolution Neural Network (DCNN)-based deep learning method to overcome the drawbacks of blurring to low-contrast edges in the image. The proposed system’s performance is validated through simulation, and the simulation results are obtained using Python simulation software. The obtained simulation results demonstrate the superiority of the proposed underwater image enhancement method. The number of different coefficients is compared with the results of the algorithm of the Underwater Image Colorfulness Measure (UICM), Underwater Image Sharpness Measure (UISM), Underwater Image Contrast Measure (UIconM) and Underwater Image Quality Measure (UIQM) values. After that, it was found to be an effective comparison of visualization techniques.