在计算机视觉和图像处理领域,图像超分辨率(Super-Resolution, SR)任务一直是一个备受关注的研究热点。其目标是从给定的低分辨率输入中重建出高分辨率的图像。随着深度学习技术的发展,特别是Transformer模型的兴起,为这一任务带来了新的解决方案。然而,现有的Transformer方法在利用输入信息时仍存在局限,无法充分发挥其潜力。
[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)
Title: Activating More Pixels in Image Super-Resolution Transformer Paper:https://arxiv.org/pdf/2205.04437.pdf Code:https://github.com/XPixelGroup/HAT 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...
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_...
[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) 代码语言:javascript
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 ...
CVPR2023 - Activating More Pixels in Image Super-Resolution Transformer Arxiv - HAT: Hybrid Attention Transformer for Image Restoration - XPixelGroup/HAT
[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)
super().__init__() self.dim = dim self.input_resolution = input_resolution self.num_heads = num_heads self.window_size = window_size self.shift_size = shift_size self.mlp_ratio = mlp_ratio if min(self.input_resolution) <= self.window_size: ...
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}, ...