  Most image matching methods perform poorly when encountering large scale
changes in images. To solve this problem, firstly, we propose a
scale-difference-aware image matching method (SDAIM) that reduces image scale
differences before local feature extraction, via resizing both images of an
image pair according to an estimated scale ratio. Secondly, in order to
accurately estimate the scale ratio, we propose a
covisibility-attention-reinforced matching module (CVARM) and then design a
novel neural network, termed as Scale-Net, based on CVARM. The proposed CVARM
can lay more stress on covisible areas within the image pair and suppress the
distraction from those areas visible in only one image. Quantitative and
qualitative experiments confirm that the proposed Scale-Net has higher scale
ratio estimation accuracy and much better generalization ability compared with
all the existing scale ratio estimation methods. Further experiments on image
matching and relative pose estimation tasks demonstrate that our SDAIM and
Scale-Net are able to greatly boost the performance of representative local
features and state-of-the-art local feature matching methods.
