Fluorescence lifetime imaging microscopy (FLIM) provides detailed information
about molecular interactions and biological processes. A major bottleneck for
FLIM is image resolution at high acquisition speeds, due to the engineering and
signal-processing limitations of time-resolved imaging technology. Here we
present single-sample image-fusion upsampling (SiSIFUS), a data-fusion approach
to computational FLIM super-resolution that combines measurements from a
low-resolution time-resolved detector (that measures photon arrival time) and a
high-resolution camera (that measures intensity only). To solve this otherwise
ill-posed inverse retrieval problem, we introduce statistically informed priors
that encode local and global dependencies between the two single-sample
measurements. This bypasses the risk of out-of-distribution hallucination as in
traditional data-driven approaches and delivers enhanced images compared for
example to standard bilinear interpolation. The general approach laid out by
SiSIFUS can be applied to other image super-resolution problems where two
different datasets are available.

通过数据融合方法，利用低分辨率时间分辨探测器和高分辨率相机的测量结果，解决 FLIM 超分辨率成像技术在高采集速度下的图像分辨率瓶颈，并引入统计信息先验来解决逆问题，从而提供比传统线性插值更清晰的图像。该方法还适用于其他存在两个不同数据集的图像超分辨率问题。

荧光寿命图像的单样本图像融合上采样

Single-sample image-fusion upsampling of fluorescence lifetime images

How can we effectively leverage the domain knowledge from remote sensing to
better segment agriculture land cover from satellite images? In this paper, we
propose a novel, model-agnostic, data-fusion approach for vegetation-related
computer vision tasks. Motivated by the various Vegetation Indices (VIs), which
are introduced by domain experts, we systematically reviewed the VIs that are
widely used in remote sensing and their feasibility to be incorporated in deep
neural networks. To fully leverage the Near-Infrared channel, the traditional
Red-Green-Blue channels, and Vegetation Index or its variants, we propose a
Generalized Vegetation Index (GVI), a lightweight module that can be easily
plugged into many neural network architectures to serve as an additional
information input. To smoothly train models with our GVI, we developed an
Additive Group Normalization (AGN) module that does not require extra
parameters of the prescribed neural networks. Our approach has improved the
IoUs of vegetation-related classes by 0.9-1.3 percent and consistently improves
the overall mIoU by 2 percent on our baseline.

本文提出了一种新颖的数据融合方法，旨在从卫星图像中更好地区分农业土地覆盖，并介绍了如何将遥感领域的领域知识与深度神经网络相结合以提高模型性能。