As an ever-increasing demand for high dynamic range (HDR) scene shooting, multi-exposure image fusion (MEF) technology has abounded. In recent years, multi-scale exposure fusion approaches based on detail-enhancement have led the way for improvement in highlight and shadow details. Most of such methods, however, are too computationally expensive to be deployed on mobile devices. This paper presents a perceptual multi-exposure fusion method that not just ensures fine shadow/highlight details but with lower complexity than detailenhanced methods. We analyze the potential defects of three classical exposure measures in lieu of using detail-enhancement component and improve two of them, namely adaptive Wellexposedness (AWE) and the gradient of color images (3-D gradient). AWE designed in YCbCr color space considers the difference between varying exposure images. 3-D gradient is employed to extract fine details. We build a large-scale multiexposure benchmark dataset suitable for static scenes, which contains 167 image sequences all told. Experiments on the constructed dataset demonstrate that the proposed method exceeds existing eight state-of-the-art approaches in terms of visually and MEF-SSIM value. Moreover, our approach can achieve a better improvement for current image enhancement techniques, ensuring fine detail in bright light.
翻译:由于对高动态范围(HDR)现场拍摄的需求不断增加,对高动态范围(HDR)现场拍摄的多曝光图像聚合(MEF)技术的需求不断增加,多曝光图像聚合(MEF)技术的多重需求日益增长。近年来,基于细节增强的多尺度接触聚合方法导致亮度和阴影细节的改进。然而,大多数这类方法在计算上太昂贵,无法在移动设备上部署,但是,由于对高动态范围(HDR)场场场拍摄的需求不断增加,对高动态范围(HDDR)现场拍摄的需求不断增加,对多曝光图像聚合(MEF)技术的需求越来越多。我们分析了三种典型暴露暴露措施的潜在缺陷,而不是使用细节增强部分,并改进了其中两种措施,即适应性健康增强(AWEW)和彩色图像梯度(3-D梯度)的梯度。在YCbcccr 色彩空间设计的AWE用于考虑不同暴露图像的差异。本文使用3-D梯度来提取精细的细节。我们为静场建立了大型多探索基准数据集数据集,其中包括167的图像序列。我们所讲了167的图象序列。我们分析了三种典型数据结构的实验表明显示显示显示显示显示显示显示显示显示显示显示显示显示,目前改进方法的改进方法的实验显示SLIM的改进方法,确保了我们目前改进的改进的改进方法的改进方法的改进方法的改进了目前的改进了目前的8号的改进方法,确保了目前的改进方法,可以实现改进的改进的改进了目前的改进。