Akram Muhammad Adeel, Akram Tallha, Javed Umer, Rafiq Muhammad, Naz Mehvish, He Di
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah, Pakistan.
Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.
PLoS One. 2024 Dec 26;19(12):e0315902. doi: 10.1371/journal.pone.0315902. eCollection 2024.
Image registration has demonstrated its significance as an essential tool for target recognition, classification, tracking, and damage assessment during natural catastrophes. The image registration process relies on the identification of numerous reliable features; thus, low resolutions, poor lighting conditions, and low image contrast substantially diminish the number of dependable features available for registration. Contrast stretching enhances image quality, facilitating the object detection process. In this study, we proposed a hybrid binary differential evolution and BAT optimization model to enhance contrast stretching by optimizing a decision variables in the transformation function. To validate its efficiency, the proposed approach is utilized as a preprocessor before feature extraction in image registration. Cross-comparison of detected features of enhanced images verses the original images during image registration validate the improvements in the image registration process.
图像配准已证明其作为自然灾害期间目标识别、分类、跟踪和损伤评估的重要工具的意义。图像配准过程依赖于众多可靠特征的识别；因此，低分辨率、恶劣的光照条件和低图像对比度会大幅减少可用于配准的可靠特征数量。对比度拉伸可提高图像质量，便于目标检测过程。在本研究中，我们提出了一种混合二进制差分进化和蝙蝠优化模型，通过优化变换函数中的决策变量来增强对比度拉伸。为验证其效率，所提出的方法在图像配准中用作特征提取前的预处理器。在图像配准期间对增强图像与原始图像检测到的特征进行交叉比较，验证了图像配准过程中的改进。
