Super-resolution image by decomposing the 2D convolution of LKA into 1-D kernels with LCAN
Super-resolution image by decomposing the 2D convolution of LKA into 1-D kernels with LCAN
Large coordinate kernel attention network for lightweight image super-resolution
arXiv paper abstract https://arxiv.org/abs/2405.09353
arXiv PDF paper https://arxiv.org/pdf/2405.09353
The multi-scale receptive field and large kernel attention (LKA) ... improve performance in ... image super-resolution task ... propose the multi-scale blueprint separable convolutions (MBSConv) as ... building block with multi-scale receptive field, it ... focus on ... learning ... multi-scale information
... revisit the key properties of LKA in which ... find that the adjacent direct interaction of local information and long-distance dependencies is crucial to provide ... performance.
... propose a large coordinate kernel attention (LCKA) module which decomposes the 2D convolutional kernels of the depth-wise convolutional layers in LKA into horizontal and vertical 1-D kernels.
LCKA enables the adjacent direct interaction of local information and long-distance dependencies not only in the horizontal direction but also in the vertical.
... LCKA allows ... large kernels in the depth-wise convolutional layers to capture ... contextual information, which ... improve the reconstruction ... and ... lower computational ... and memory ...
Integrating MBSConv and LCKA, ... propose a large coordinate kernel attention network (LCAN) ... LCAN with the lowest model complexity achieves superior performance ...
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