Activating More Pixels in Image Super-Resolution Transformer Xiangyu Chen, Xintao Wang, Jiantao Zhou and Chao Dong BibTeX @article{chen2022activating, title={Activating More Pixels in Image Super-Resolution Transformer}, author={Chen, Xiangyu and Wang, Xintao and Zhou, Jiantao and Dong, Chao}, ...
Title: Activating More Pixels in Image Super-Resolution TransformerPaper: arxiv.org/pdf/2205.0443Code: github.com/XPixelGroup/ 导读 本文提出了一种名为Hybrid Attention Transformer (HAT)的方法,旨在通过结合深度学习技术和注意力机制来改进图像超分辨率任务。 单图像超分辨率(SR)任务是计算机视觉和图像处理领域...
图像重建(Image Reconstruction): 最后,经过深层特征提取的特征通过一个图像重建模块,将高层次特征转化为输出的超分辨率图像。 yolov10 引入 classHAT(nn.Module):r"""混合注意力变换器 (Hybrid Attention Transformer) 该PyTorch实现基于 `Activating More Pixels in Image Super-Resolution Transformer`。 部分代码基于...
EHAT:Enhanced Hybrid Attention Transformer for Remote Sensing Image Super-Resolutiondoi:10.1007/978-981-97-8685-5_16In recent years, deep learning (DL)-based super-resolution techniques for remote sensing images have made significant progress. However, these models have constraints in effectively ...
该PyTorch实现基于 `Activating More Pixels in Image Super-Resolution Transformer`。 部分代码基于SwinIR。 参数: img_size (int | tuple(int)): 输入图像大小。默认值64 patch_size (int | tuple(int)): Patch大小。默认值1 in_chans (int): 输入图像通道数。默认值3 ...
and image pre-training for low-level vision. arXiv preprint, arXiv:2112.10175 (2021)[4] Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9199–9208 (2021)
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image into non-overlapping patches self.patch_embed = PatchEmbed( img_size=img_size, patch_size=patch_size, in_chans=embed_dim, embed_dim=embed_dim, norm_layer=norm_layer if self.patch_norm else None) num_patches = self.patch_embed.num_patches patches_resolution = self.patch_embed....
[3] Li, W., Lu, X., Lu, J., Zhang, X., Jia, J.: On efficient transformer and image pre-training for low-level vision. arXiv preprint, arXiv:2112.10175 (2021) [4] Gu, J., Dong, C.: Interpreting super-resolution networks with local attribution maps. In: Proceedings of the IEE...
[3] Li, W., Lu, X., Lu, J., Zhang, X., Jia, J.: On efficient transformer and image pre-training for low-level vision. arXiv preprint, arXiv:2112.10175 (2021) [4] Gu, J., Dong, C.: Interpreting super-resol...