This paper addresses the problem of visible (RGB) to Near-Infrared (NIR)
image fusion. Multispectral imaging is an important task relevant to image
processing and computer vision, even more, since the development of the RGBT
sensor. While the visible image sees color and suffers from noise, haze, and
clouds, the NIR channel captures a clearer picture and it is significantly
required by applications such as dehazing or object detection. The proposed
approach fuses these two aligned channels by training a
Convolutional-Neural-Network (CNN) by a Self-Supervised-Learning (SSL) on a
single example. For each such pair, RGB and IR, the network is trained for
seconds to deduce the final fusion. The SSL is based on Sturcture-of-Similarity
(SSIM) loss combined with Edge-Preservation (EP) loss. The labels for the SSL
are the input channels themselves. This fusion preserves the relevant detail of
each spectral channel while not based on a heavy training process. In the
experiments section, the proposed approach achieves better qualitative and
quantitative multispectral fusion results with respect to other recent methods,
that are not based on large dataset training.

该研究提出了一种可见光与近红外图像融合的方法，通过使用 RGBT 传感器获取的可见图像和近红外通道的信息，利用 SSIM 损失和边缘保护等技术进行融合，并在实验证明相比其他方法，该方法在多光谱融合方面取得了更好的结果。

基于单一示例的可见光和红外自我监督融合训练

Visible and infrared self-supervised fusion trained on a single example

Edge-preserving image smoothing is a fundamental procedure for many computer
vision and graphic applications. There is a tradeoff between the smoothing
quality and the processing speed: the high smoothing quality usually requires a
high computational cost which leads to the low processing speed. In this paper,
we propose a new global optimization based method, named iterative least
squares (ILS), for efficient edge-preserving image smoothing. Our approach can
produce high-quality results but at a much lower computational cost.
Comprehensive experiments demonstrate that the propose method can produce
results with little visible artifacts. Moreover, the computation of ILS can be
highly parallel, which can be easily accelerated through either multi-thread
computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is
able to process images of 1080p resolution ($1920\times1080$) at the rate of
20fps for color images and 47fps for gray images. In addition, the ILS is
flexible and can be modified to handle more applications that require different
smoothing properties. Experimental results of several applications show the
effectiveness and efficiency of the proposed method. The code is available at
https://github.com/wliusjtu/Real-time-Image-Smoothing-via-Iterative-Least-Squares

提出了一种名为迭代最小二乘法（ILS）的新全局优化方法，可用于高效的边缘保留图像平滑，并能通过 GPU 加速从而显著提高处理速度。实验结果表明该方法在处理高质量图像时具有较少的可见伪影。

迭代最小二乘实现实时图像平滑处理

Real-time Image Smoothing via Iterative Least Squares

In this paper, we propose a novel edge preserving and multi-scale contextual
neural network for salient object detection. The proposed framework is aiming
to address two limits of the existing CNN based methods. First, region-based
CNN methods lack sufficient context to accurately locate salient object since
they deal with each region independently. Second, pixel-based CNN methods
suffer from blurry boundaries due to the presence of convolutional and pooling
layers. Motivated by these, we first propose an end-to-end edge-preserved
neural network based on Fast R-CNN framework (named RegionNet) to efficiently
generate saliency map with sharp object boundaries. Later, to further improve
it, multi-scale spatial context is attached to RegionNet to consider the
relationship between regions and the global scenes. Furthermore, our method can
be generally applied to RGB-D saliency detection by depth refinement. The
proposed framework achieves both clear detection boundary and multi-scale
contextual robustness simultaneously for the first time, and thus achieves an
optimized performance. Experiments on six RGB and two RGB-D benchmark datasets
demonstrate that the proposed method achieves state-of-the-art performance.

本研究提出了一种新的神经网络，用于保护边缘和多尺度语境下的显著对象检测。该框架旨在解决现有的基于 CNN 的方法存在的两个限制：区域 CNN 方法缺乏足够的上下文来准确定位显著对象，而基于像素的 CNN 方法由于卷积和池化层的存在而具有模糊的边界。该方法可广泛应用于 RGB-D 显著性检测，并具有清晰的检测边界和多尺度语境下的鲁棒性。实验结果显示，该方法在六个 RGB 和两个 RGB-D 基准数据集上均取得了最优性能。