Department of Electrical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
Information Systems Department, College of Computer and Information Sciences, Imam Mohammed Bin Saud Islamic University, KSA, Riyadh, Saudi Arabia.
Big Data. 2023 Jun;11(3):215-224. doi: 10.1089/big.2021.0223. Epub 2023 Mar 16.
Multiexposure image fusion (MEF) is an effective approach to generate high dynamic range images from multilevel exposures taken from ordinary cameras. In this article, a novel MEF algorithm is proposed to gain maximum visual details as well as vivid colors from the captured scene. This algorithm first decomposes the input images with multiple exposures into the base and detail layer. The weights for the base and detail layers are computed by using exposedness function and then both the layers are combined to generate the final fused image. The proposed multiexposure technique requires fewer computational operations, preserves edges, and also reduces spatial artifacts. The proposed technique has been evaluated quantitatively using image quality assessment model based on structure similarity index measure for MEF. By the extensive experimental results, it has been illustrated that in addition to significantly outperforming other state-of-the-art techniques, the proposed technique is much faster and can achieve better image quality.
多曝光图像融合（MEF）是一种从普通相机拍摄的多灰度级曝光中生成高动态范围图像的有效方法。本文提出了一种新的 MEF 算法，旨在从捕获的场景中获得最大的视觉细节和生动的色彩。该算法首先将输入的多曝光图像分解为基础层和细节层。使用暴露函数计算基础层和细节层的权重，然后将这两个层组合以生成最终的融合图像。所提出的多曝光技术需要较少的计算操作，保留边缘，并且还减少空间伪影。使用基于结构相似性指数度量的图像质量评估模型对 MEF 进行了定量评估。通过广泛的实验结果表明，除了显著优于其他最先进的技术外，该技术还更快，并且可以获得更好的图像质量。
