改进PDE模型与快速近似算法，提升雾霾/水下/沙尘图像增强效果。
Improved Partial Differential Equation and Fast Approximation Algorithm for Hazy/Underwater/Dust Storm Image Enhancement
- 结合对数图像处理与线性滤波器优化PDE模型，提升去雾精度。
- 有效避免图像发暗、边缘过增强及天空区域过度增强问题。
- 适用于水下和沙尘场景，适合图像增强与视觉质量提升研究者。
本文提出一种改进的基于偏微分方程（PDE）的去雾算法。该方法将对数图像处理模型融入PDE框架，并通过空间或频域的线性滤波器进行优化。同时，开发了一种针对雾霾图像形成模型的快速简化近似函数，结合模糊同态优化策略。所提算法有效解决了以往方法中存在的图像发暗、边缘过增强以及暗区增强不足等问题，同时避免了天空区域过度增强和伪影（halo）效应。此外，算法进一步拓展至水下与沙尘场景，引入改进的全局对比度增强机制。实验结果表明，该方法在多个量化图像质量指标上优于文献中多数现有算法。
原文摘要 · Abstract (English)
This paper presents an improved and modified partial differential equation (PDE)-based de-hazing algorithm. The proposed method combines logarithmic image processing models in a PDE formulation refined with linear filter-based operators in either spatial or frequency domain. Additionally, a fast, simplified de-hazing function approximation of the hazy image formation model is developed in combination with fuzzy homomorphic refinement. The proposed algorithm solves the problem of image darkening and over-enhancement of edges in addition to enhancement of dark image regions encountered in previous formulations. This is in addition to avoiding enhancement of sky regions in de-hazed images while avoiding halo effect. Furthermore, the proposed algorithm is utilized for underwater and dust storm image enhancement with the incorporation of a modified global contrast enhancement algorithm. Experimental comparisons indicate that the proposed approach surpasses a majority of the algorithms from the literature based on quantitative image quality metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。