Analysis of proposed PDE-based underwater image enhancement algorithms

TL;DR

This paper presents PDE-based underwater image enhancement algorithms that integrate smoothing, contrast, and color correction, including a hybrid approach with CLAHE, optimized for improved visual and quantitative results.

Contribution

The paper introduces a unified PDE framework combining smoothing, contrast, and color enhancement, incorporating a dynamic CLAHE component for superior underwater image enhancement.

Findings

Algorithms effectively perform illumination correction, color restoration, and noise suppression.

Proposed methods outperform conventional underwater image enhancement algorithms.

Hybrid approach reduces noise amplification while enhancing image quality.

Abstract

This report describes the experimental analysis of proposed underwater image enhancement algorithms based on partial differential equations (PDEs). The algorithms perform simultaneous smoothing and enhancement due to the combination of both processes within the PDE-formulation. The framework enables the incorporation of suitable colour and contrast enhancement algorithms within one unified functional. Additional modification of the formulation includes the combination of the popular Contrast Limited Adaptive Histogram Equalization (CLAHE) with the proposed approach. This modification enables the hybrid algorithm to provide both local enhancement (due to the CLAHE) and global enhancement (due to the proposed contrast term). Additionally, the CLAHE clip limit parameter is computed dynamically in each iteration and used to gauge the amount of local enhancement performed by the CLAHE within the formulation. This enables the algorithm to reduce or prevent the enhancement of noisy artifacts, which if present, are also smoothed out by the anisotropic diffusion term within the PDE formulation. In other words, the modified algorithm combines the strength of the CLAHE, AD and the contrast term while minimizing their weaknesses. Ultimately, the system is optimized using image data metrics for automated enhancement and compromise between visual and quantitative results. Experiments indicate that the proposed algorithms perform a series of functions such as illumination correction, colour enhancement correction and restoration, contrast enhancement and noise suppression. Moreover, the proposed approaches surpass most other conventional algorithms found in the literature.