Underwater Image Enhancement Methods Using Biovision and Type-II Fuzzy Set

Abstract

Accurately extracting underwater images has never been more challenging, as the lack of clarity of detail due to issues such as scattering and light absorption is more noticeable than ever before. This research method addresses these problems while clarifying the limitations of existing methods and proposes a comprehensive approach to underwater image processing. Current methods tend to focus only on the effects of individual factors, such as color shifts, visibility, or contrast enhancement, and do not take into account biological vision applications. In contrast, the method proposed in this paper applies a color correction module that takes into account the effects of biological vision in LAB color space, and an enhanced Type-II Fuzzy set visibility enhancement module. This synchronized approach overcomes the limitations of the previous methods in that the contrast enhancement utilizes a curve transform and a multi-scale fusion strategy that preserves the essential image details. The framework not only adjusts the overall image features, but also finely handles the local details, resulting in a significant enhancement of both the overall quality and the local detail clarity of underwater images. The experimental results demonstrate that the application of the method of this study on two datasets gives results that are better than those of the top 10 existing algorithms. By explicitly addressing the limitations of existing methods, the method becomes an advantageous solution in underwater image processing, providing enhancements in image quality and task-specific applications in a concise and efficient manner.