5 accepted papers
Pansharpening is a crucial and challenging task that aims to obtain a high spatial resolution image by merging a multispectral (MS) image and a panchromatic (PAN) image. Current methods use CNNs with standard convolution, but we've observed strong correlation among channel dimensions in the kernel,…
Despite the success of vision Transformers for the image deraining task, they are limited by computation-heavy and slow runtime. In this work, we investigate Transformer decoder is not necessary and has huge computational costs. Therefore, we revisit the standard vision Transformer as well as its su…
Pansharpening is a critical yet challenging low-level vision task that aims to obtain a higher-resolution image by fusing a multispectral (MS) image and a panchromatic (PAN) image. While most pansharpening methods are based on convolutional neural network (CNN) architectures with standard convolutio…
Standard convolution operations can effectively perform feature extraction and representation but result in high computational cost, largely due to the generation of the original convolution kernel corresponding to the channel dimension of the feature map, which will cause unnecessary redundancy. In…
Deep Convolution Neural Networks have been adopted for pansharpening and achieved state-of-the-art performance. However, most of the existing works mainly focus on single-scale feature fusion, which leads to failure in fully considering relationships of information between high-level semantics and l…