  The convolution operation is a powerful tool for feature extraction and plays
a prominent role in the field of computer vision. However, when targeting the
pixel-wise tasks like image fusion, it would not fully perceive the
particularity of each pixel in the image if the uniform convolution kernel is
used on different patches. In this paper, we propose a local adaptive
convolution (LAConv), which is dynamically adjusted to different spatial
locations. LAConv enables the network to pay attention to every specific local
area in the learning process. Besides, the dynamic bias (DYB) is introduced to
provide more possibilities for the depiction of features and make the network
more flexible. We further design a residual structure network equipped with the
proposed LAConv and DYB modules, and apply it to two image fusion tasks.
Experiments for pansharpening and hyperspectral image super-resolution (HISR)
demonstrate the superiority of our method over other state-of-the-art methods.
It is worth mentioning that LAConv can also be competent for other
super-resolution tasks with less computation effort.
