1. DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs [DeepFuse(ICCV 2017)] [Paper] [Code] 2. Multi-exposure fusion with CNN features [CNN(ICIP 2018)] [Paper] [Code] 3. Deep guided learning for fast multi-exposure image fusion [DeepFuse(MEF-Net(...
[10] H. Li, L. Zhang, Multi-exposure Fusion with CNN Features, in: 2018 25th IEEE International Conference on Image Processing, 2018, pp. 1723–1727. [11] N. Hayat, M. Imran, Ghost-free multi exposure image fusion technique using dense sift descriptor and guided filter, J. Vis. Comm...
In this paper, we propose TransMEF, a transformer-based multi-exposure image fusion framework that uses self-supervised multi-task learning. The framework is based on an encoder-decoder network, which can be trained on large natural image datasets and does not require ground truth fusion images....
AAAI 2020EEMEFN:Low-LightImageEnhancementviaEdge-EnhancedMulti-ExposureFusionNetwork...enhancement.在检测步骤中,首先从初始图像生成边缘映射,而不是从输入图像生成边缘映射。输入图像和多次曝光图像的噪声非常大,使得边缘提取非常困难。MEF模块有效地去除噪音,并生成清晰的法线光图像和精确的边缘 ...
Multi-exposure fusion with CNN features. In Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 7–10 October 2018; pp. 1723–1727. [Google Scholar] Xu, H.; Ma, J.; Zhang, X.-P. MEF-GAN: Multi-Exposure Image Fusion via Generative ...
Multi-exposure fusion with CNN features. In Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 7–10 October 2018; pp. 1723–1727. [Google Scholar] Lahoud, F.; Süsstrunk, S. Fast and efficient zero-learning image fusion. arXiv 2019, arXiv...
Multi-Exposure Fusion with CNN Features. In Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 7–10 October 2018; pp. 1723–1727. [Google Scholar] [CrossRef] Van Aardt, J. Assessment of image fusion procedures using entropy, image quality...
In the intelligent transportation system, as a type of important road information, traffic signs are fused by this method to obtain a fused image with moderate brightness and intact information. By estimating the degree of retention of different features in the source image, the fusion results ...
It incorporates neural networks with memory unit structures into image fusion, enhancing the ability of network to extract and fuse image features. It should be noted that the weight setting of the MUFusion loss function is not designed for MFIF. Interestingly, regression-based methods such as ...
In contrast, positive clinical outcomes were associated with 50% fT>MIC and 100% fT>MIC ratios (OR, 1.02 and 1.56, respectively; p < 0.03) [3]. Moreover, this study reported large variability β-lactams plasma concentration, which was attenuated via a prolonged infusion (either extended in...