SUNet: Swin Transformer UNet for Image Denoising 来自 arXiv.org 喜欢 0 阅读量: 779 作者:CM Fan,TJ Liu,KH Liu 摘要: Image restoration is a challenging ill-posed problem which also has been a long-standing issue. In the past few years, the convolution neural networks (CNNs) almost ...
[ISCAS 2022]SUNet: Swin Transformer with UNet for Image Denoising Chi-Mao Fan, Tsung-Jung Liu, Kuan-Hsien Liu Abstract : Image restoration is a challenging ill-posed problem which also has been a long-standing issue. In the past few years, the convolution neural networks (CNNs) almost domi...
SwinIR直接将2D版的Swin Transformer应用到图像复原领域,在各个子领域取得了“全面领先”的性能,同时也获得了ICCV-AIM2021最佳论文奖。 在Swin Transformer+Image Restoration组合方面尝到“甜头”后,SwinIR的作者又瞄上了3D版组合:Video Swin Transformer + Video Restoration,又一次刷新了视频复原多个领域的“高度”。
从而降低图片的复杂度,但这一系列工作虽然逻辑上通畅,但因为硬件上无法加速等,导致模型没法太大总之,之前的工作要么把CNN和self-attention结合起来,要么把self-attention取代CNN,但都没取得很好的扩展效果,看来得再次冲击transformer for CV
Convolution layers are used for extracting spatial features, and 2. Swin transformer is used for image reconstruction. 3. Using UNet for high-level deep feature extraction. In this research, denoising is performed on the artificial noisy MRI pictures. Up and down sampling are used in the UNet ...
我们将使用预训练的SwinUNet模型进行图像去噪。以下是一个简单的SwinUNet模型定义和加载预训练权重的代码。 在这里插入图片描述 SwinUNet 模型定义 importtorchimporttorch.nnasnnfromeinopsimportrearrange,repeatfromeinops.layers.torchimportRearrangefromtimm.models.vision_transformerimportDropPath,MlpclassPatchEmbed(nn.Mo...
(SC) block as the main building block of a UNet backbone. In each SC block, the input is first passed through a 1×1 convolution, and subsequently is split evenly into two feature map groups, each of which is then fed into a swin transformer (SwinT) block and residual 3×3 ...
Furthermore, the Efficient UNet leveraged by Imagen is not the best choice in image processing. To address these issues, we propose the Swinv2-Imagen, a novel text-to-image diffusion model based on a Hierarchical Visual Transformer and a Scene Graph incorporating a semantic layout. In the ...
SCUNet exploits the swin-conv (SC) block as the main building block of a UNet backbone. In each SC block, the input is first passed through a 1×1 convolution, and subsequently is split evenly into two feature map groups, each of which is then fed into a swin transformer (SwinT) ...
for blind real image denoising. News (2022-03-23): We releasethe testing codesofSCUNet for blind real image denoising. The following results are obtained by our SCUNet with purely synthetic training data! We did not use the paired noisy/clean data by DND and SIDD during training!