Perceptual Quality Assessment for Multi-Exposure Image Fusion论文解读 本篇论文致力于为多曝光图像融合(Multi-Exposure image Fusion, MEF)领域开发一种更客观、更可信,并且不依赖于ground true HDR图像的图像质量评价指标(Image Quality Assessment, IQA),其目标是给各类MEF算法提供更客观的评价模型。 本文主要贡献 ...
基于深度学习的多曝光图像融合(Multi-exposure image fusion)算法论文及代码整理 首先附上近期整理基于深度学习的图像融合算法的思维导图 本篇文章主要整理整理基于深度学习的多曝光图像融合(Multi-exposure image…
SPD-MEF的主要思想是:将图像块解构为三个概念独立的部分,信号强度(signal strength)、信号结构(signal structure)、平均强度(mean intensity),分别进行融合处理后再还原到融合图像中。 Multi-exposure image fusion(MEF)提供了一种经济高效的方式来克服HDR成像与LDR显示之间的矛盾,将具有不同曝光度层次的源图像序列作为...
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images(TIP18) 这是一篇单一图像对比度增强的论文,传统的单一图像对比度增强方法包括基于HE和Retinex理论,但由于自然场景的复杂性和单张图像包含的信息有限,往往很难产生高质量的结果。因此有了基于多曝光图像序列的图像增强,主要有多曝光图像融合(MEF...
We propose a patch-wise approach for multi-exposure image fusion (MEF). A key step in our approach is to decompose each color image patch into three conceptually independent components: signal strength, signal structure and mean intensity. Upon processing the three components separately based on ...
We propose a multi-exposure image fusion (MEF) algorithm by optimizing a novel objective quality measure, namely the color MEF structural similarity (MEF-SSIMc) index. The design philosophy we introduce here is substantially different from existing ones. Instead of pre-defining a systematic computati...
MEFB is the first benchmark in the field of multi-exposure image fusion (MEF), aiming to provide a platform to perform fair and comprehensive performance comparision of MEF methods. Currently,100 image pairs, 21 fusion algorithms and 20 evaluation metricsare integrated in MEFB, which can be ...
We propose a simple yet effective structural patch decomposition approach for multi-exposure image fusion (MEF) that is robust to ghosting effect. We decompose an image patch into three conceptually independent components: signal strength, signal structure, and mean intensity. Upon fusing these three ...
Thirteen representative multi-exposure image fusion and stack-based high dynamic range imaging algorithms are employed to generate the contrast enhanced images for each sequence, and subjective experiments are conducted to screen the best quality one as the reference image of each scene. With the ...
11.S. Li and X. Kang, "Fast multi-exposure image fusion with median filter and recursive filter",IEEE Trans. Consum. Electron., vol. 58, no. 2, pp. 626-632, May 2012. 12.S. Li, X. Kang and J. Hu, "Image fusion with guided filtering",IEEE Trans. Image Process., vol. 22,...