IEEE Trans Image Process. 2017 Mar;26(3):1243-1252. doi: 10.1109/TIP.2017.2651366. Epub 2017 Jan 16.
Multi-scale exposure fusion is an effective image enhancement technique for a high dynamic range (HDR) scene. In this paper, a new multi-scale exposure fusion algorithm is proposed to merge differently exposed low dynamic range (LDR) images by using the weighted guided image filter to smooth the Gaussian pyramids of weight maps for all the LDR images. Details in the brightest and darkest regions of the HDR scene are preserved better by the proposed algorithm without relative brightness change in the fused image. In addition, a new weighted structure tensor is introduced to the differently exposed images and it is adopted to design a detail extraction component for the proposed fusion algorithm, such that users are allowed to manipulate fine details in the enhanced image according to their preference. The proposed multi-scale exposure fusion algorithm is also applied to design a simple single image brightening algorithm for both low-light imaging and back-light imaging.
多尺度曝光融合是一种有效的高动态范围（HDR）场景图像增强技术。本文提出了一种新的多尺度曝光融合算法，通过使用加权引导图像滤波器对所有低动态范围（LDR）图像的权值图的高斯金字塔进行平滑，从而融合不同曝光的 LDR 图像。与融合图像的相对亮度变化相比，该算法可以更好地保留 HDR 场景中最亮和最暗区域的细节。此外，还将一种新的加权结构张量引入到不同曝光的图像中，并将其应用于所提出的融合算法的细节提取组件中，以便用户可以根据自己的喜好对增强后的图像中的精细细节进行操作。所提出的多尺度曝光融合算法也被应用于设计一种简单的单图像增亮算法，用于低光照成像和逆光成像。
