ABSTRACT

Haze is a common atmospheric disturbance that adversely affects the quality of optical data, thus often restricting their usability. Since these effects are inherent in the process of spaceborne Earth sensing, it is important to develop effective methods to remove them. This work proposes a novel method for de-hazing satellite imagery and outdoor camera images. It is developed by modifying the transmission map used in Dark Channel Prior (DCP) method. A Weighted Variance Guided Filter (WVGF) is introduced for enhancing the image quality, which included a two-stage image decomposition and fusion process. The method also optimally combines the radiance and transmission components along with an additional stage modelling a fusion-based transparency function. A final guided filter-based image refinement scheme is incorporated to improve the processed image quality. The optimal tuning of the image-dependent parameters at various stages is achieved using the newly proposed Adaptive Black Widow Optimization (ABWO) algorithm, which makes the proposed de-hazing scheme fully automatic. Qualitative and quantitative performance analyses, and the results are compared with other state-of-the-art methods. The experimental results reveal that the proposed method performs better as compared with others, independent of the haze density, without losing the natural look of the scene.

摘要

雾度是一种常见的大气干扰，会对光学数据的质量产生不利影响，因此通常会限制其可用性。由于这些效应是星载地球感测过程中固有的，因此开发有效的方法来消除它们很重要。这项工作提出了一种对卫星图像和室外摄像机图像进行除雾的新方法。它是通过修改暗通道优先（DCP）方法中使用的传输图来开发的。引入了加权方差引导滤波器（WVGF）来增强图像质量，其中包括两阶段的图像分解和融合过程。该方法还可以最佳地组合辐射和透射分量以及对基于融合的透明函数建模的附加阶段。最终的基于引导滤波器的图像细化方案被并入以改善处理后的图像质量。使用新提出的自适应黑寡妇优化（ABWO）算法可实现各个阶段图像相关参数的最佳调整，从而使所提出的除雾方案实现全自动。定性和定量性能分析，并将结果与​​其他最新方法进行比较。实验结果表明，所提出的方法与其他方法相比具有更好的性能，与雾度无关，而不会丢失场景的自然外观。定性和定量性能分析，并将结果与​​其他最新方法进行比较。实验结果表明，所提出的方法与其他方法相比具有更好的性能，与雾度无关，而不会丢失场景的自然外观。定性和定量性能分析，并将结果与​​其他最新方法进行比较。实验结果表明，所提出的方法与其他方法相比具有更好的性能，与雾度无关，而不会丢失场景的自然外观。