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

The goal of video motion magnification techniques is to magnify small motions
in a video to reveal previously invisible or unseen movement. Its uses extend
from bio-medical applications and deep fake detection to structural modal
analysis and predictive maintenance. However, discerning small motion from
noise is a complex task, especially when attempting to magnify very subtle
often sub-pixel movement. As a result, motion magnification techniques
generally suffer from noisy and blurry outputs. This work presents a new
state-of-the-art model based on the Swin Transformer, which offers better
tolerance to noisy inputs as well as higher-quality outputs that exhibit less
noise, blurriness and artifacts than prior-art. Improvements in output image
quality will enable more precise measurements for any application reliant on
magnified video sequences, and may enable further development of video motion
magnification techniques in new technical fields.

本研究提出了一种基于 Swine Transformer 的视频运动放大技术的新模型，具有更好的对噪声输入的容忍性和输出高品质荧光的能力，可用于在任何依赖放大视频序列的应用程序中实现更精确的测量，为视频运动放大技术在新技术领域的进一步发展打下基础。