Abstract
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.
Abstract (translated)
本文提出了一种改进和修改的基于偏微分方程（PDE）的去雾算法。所提方法结合了对数图像处理模型，并在空间或频域中使用线性滤波器操作符进行了优化，以形成PDE公式。此外，还开发了一种快速简化的模糊同态精炼组合式去雾函数近似，用于模拟有雾图像的生成过程。该算法解决了以前方法中存在的图像变暗和边缘过度增强的问题，并且在去除雾霾的同时避免了天空区域的过度增强以及晕轮效应。另外，在结合改进的全局对比度增强算法后，所提算法也被应用于水下和沙尘暴图像的优化处理中。实验比较表明，根据定量图像质量指标，该方法优于大多数文献中的算法。
URL
https://arxiv.org/abs/2502.15986
