Learning-Based Dark and Blurred Underwater Image Restoration

Abstract

Underwater image processing is a difficult subtopic in the field of computer vision due to the complex underwater environment. Since the light is absorbed and scattered, underwater images have many distortions such as underexposure, blurriness, and color cast. The poor quality hinders subsequent processing such as image classification, object detection, or segmentation. In this paper, we propose a method to collect underwater image pairs by placing two tanks in front of the camera. Due to the high-quality training data, the proposed restoration algorithm based on deep learning achieves inspiring results for underwater images taken in a low-light environment. The proposed method solves two of the most challenging problems for underwater image: darkness and fuzziness. The experimental results show that the proposed method surpasses most other methods.

Underwater Image Restoration and Enhancement: A Comprehensive Review of Recent Trends, Challenges, and Applications

“…Blurring is used for removing the edges to smooth an image. In the case of underwater images, blurring degrades them and obscures detail [169][170][171]. Because light is scattered and absorbed, underwater images are severely distorted by blurring and color cast, which decreases the image quality.…”