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The convolution operation is a powerful tool for feature extraction and playsa prominent role in the field of computer vision. However, when targeting thepixel-wise tasks like image fusion, it would not fully perceive theparticularity of each pixel in the image if the uniform convolution kernel isused on different patches. In this paper, we propose a local adaptiveconvolution (LAConv), which is dynamically adjusted to different spatiallocations. LAConv enables the network to pay attention to every specific localarea in the learning process. Besides, the dynamic bias (DYB) is introduced toprovide more possibilities for the depiction of features and make the networkmore flexible. We further design a residual structure network equipped with theproposed 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 othersuper-resolution tasks with less computation effort.

LAConv: Local Adaptive Convolution for Image Fusion