在嵌入 ECA（Efficient Channel Attention）注意力机制到空域降噪模块中时，可以按照以下步骤进行：

ECA注意力机制的实现

ECA注意力机制是一种高效的通道注意力机制，主要通过对每个通道进行全局平均池化，然后使用一维卷积来捕获每个通道之间的依赖关系。下面是 ECA模块的实现：

import torch
import torch.nn as nn

class ECALayer(nn.Module):
    def __init__(self, channel, k_size=3):
        super(ECALayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        # 全局平均池化
        y = self.avg_pool(x)
        # 转换形状以适应1D卷积
        y = y.squeeze(-1).transpose(-1, -2)
        # 1D卷积
        y = self.conv(y)
        # 转换回原形状
        y = y.transpose(-1, -2).unsqueeze(-1)
        # Sigmoid激活
        y = self.sigmoid(y)
        return x * y.expand_as(x)

将ECA注意力机制嵌入到去噪网络中

将 ECA注意力机制嵌入到现有的去噪网络中，我们可以在每个卷积层之后添加 ECA模块。下面是修改后的去噪网络：

import torch
import torch.nn as nn
import torch.nn.functional as F

class WeightGuidedDenoiseNetWithECA(nn.Module):
    def __init__(self):
        super(WeightGuidedDenoiseNetWithECA, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.eca1 = ECALayer(64)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.eca2 = ECALayer(64)
        self.conv3 = nn.Conv2d(64, 3, kernel_size=3, padding=1)
    
    def forward(self, x, motion_map):
        out = F.relu(self.conv1(x))
        out = self.eca1(out)
        out = F.relu(self.conv2(out))
        out = self.eca2(out)
        
        # 动态调整输出特征图
        weighted_out = out * motion_map + x * (1 - motion_map)
        
        out = self.conv3(weighted_out)
        return out

综合应用

结合 ECA注意力机制的空域去噪网络可以使用如下代码进行去噪处理：

def denoise_image(current_frame, prev_frame, next_frame, model):
    motion_map = generate_motion_map(current_frame, prev_frame, next_frame)
    
    denoised_image = model(current_frame, motion_map)
    return denoised_image

执行示例

假设 current_frame 是当前帧，prev_frame 是前一帧，next_frame 是下一帧，model 是带有 ECA注意力机制的去噪网络：

model = WeightGuidedDenoiseNetWithECA()
current_frame = torch.randn(1, 3, 256, 256)  # 示例当前帧
prev_frame = torch.randn(1, 3, 256, 256)  # 示例前一帧
next_frame = torch.randn(1, 3, 256, 256)  # 示例下一帧

denoised_image = denoise_image(current_frame, prev_frame, next_frame, model)

通过这种方式，可以利用 ECA注意力机制增强网络对通道之间依赖关系的捕获能力，从而提升空域去噪效果。