The dual camera system of wide-angle ($\bf{W}$) and telephoto ($\bf{T}$)
cameras has been widely adopted by popular phones. In the overlap region,
fusing the $\bf{W}$ and $\bf{T}$ images can generate a higher quality image.
Related works perform pixel-level motion alignment or high-dimensional feature
alignment of the $\bf{T}$ image to the view of the $\bf{W}$ image and then
perform image/feature fusion, but the enhancement in occlusion area is
ill-posed and can hardly utilize data from $\bf{T}$ images. Our insight is to
minimize the occlusion area and thus maximize the use of pixels from $\bf{T}$
images. Instead of insisting on placing the output in the $\bf{W}$ view, we
propose a view transition method to transform both $\bf{W}$ and $\bf{T}$ images
into a mixed view and then blend them into the output. The transformation ratio
is kept small and not apparent to users, and the center area of the output,
which has accumulated a sufficient amount of transformation, can directly use
the contents from the T view to minimize occlusions. Experimental results show
that, in comparison with the SOTA methods, occlusion area is largely reduced by
our method and thus more pixels of the $\bf{T}$ image can be used for improving
the quality of the output image.

使用双摄像头系统，通过图像融合方法，最大限度地利用来自 T 图像的像素以减少闲置区域，提高输出图像质量。

基于视角转换的双摄像机图像融合

View Transition based Dual Camera Image Fusion

Aerial surveillance requires high spatio-temporal resolution (HSTR) video for
more accurate detection and tracking of objects. This is especially true for
wide-area surveillance (WAS), where the surveyed region is large and the
objects of interest are small. This paper proposes a dual camera system for the
generation of HSTR video using reference-based super-resolution (RefSR). One
camera captures high spatial resolution low frame rate (HSLF) video while the
other captures low spatial resolution high frame rate (LSHF) video
simultaneously for the same scene. A novel deep learning architecture is
proposed to fuse HSLF and LSHF video feeds and synthesize HSTR video frames at
the output. The proposed model combines optical flow estimation and
(channel-wise and spatial) attention mechanisms to capture the fine motion and
intricate dependencies between frames of the two video feeds. Simulations show
that the proposed model provides significant improvement over existing
reference-based SR techniques in terms of PSNR and SSIM metrics. The method
also exhibits sufficient frames per second (FPS) for WAS when deployed on a
power-constrained drone equipped with dual cameras.

本文提出了一种用于航空监视的双摄像头系统，通过基于参考的超分辨率生成高空间 - 时间分辨率视频帧，并结合光流估计和注意机制，以捕捉两个视频源帧之间的细微运动和复杂依赖关系。通过模拟实验，证明该模型在 PSNR 和 SSIM 度量方面相较现有的基于参考的超分辨率技术取得显著改进，并在功耗受限的无人机上部署时表现出足够的帧率适用于广域航空监视。