paper：HALFWAVELET ATTENTION ON M-NET+ FOR LOW-LIGHT IMAGE ENHANCEMENT

Code：https://github.com/FanChiMao/HWMNet

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1、Half Wavelet Attention

传统的图像增强方法主要关注图像在空间域的特征信息，而忽略了时频域上的特征信息。而小波变换能够将图像分解为不同频率的子带，从而在时频域上分析图像特征，捕获图像的细节信息。所以，这篇论文提出一种 半小波注意力（Half Wavelet Attention），旨在利用小波变换的优势，从另一个维度提取图像特征，丰富特征表达，从而提升低光图像增强的效果。

HWA 的核心思想是利用小波变换在时频域的特性，提取图像在另一维度上的特征信息，从而丰富图像的特征表达，提升低光图像增强的效果。HWA 模块通过将输入特征图分为两部分，一部分保持不变，另一部分进行离散小波变换，得到小波域特征图。

对于输入X，HWA 的实现过程：

1. 特征分割： 将输入特征图沿通道维度分为两部分，一部分保持不变，另一部分进行离散小波变换。
2. 注意力机制： 对小波域特征图进行通道注意力和空间注意力操作，提取加权特征图。
3. 逆小波变换： 将加权小波域特征图进行逆小波变换，得到加权空间域特征图。
4. 特征融合： 将加权空间域特征图与保持不变的特征图进行拼接，并进行残差连接和跳跃连接，得到最终的输出特征图。

HWA 的主要优势：

1. 丰富特征表达： HWA 模块能够从另一个维度提取图像特征，丰富特征表达，从而提升低光图像增强的效果。
2. 提升细节信息： 小波变换能够捕获图像的细节信息，HWA 模块能够有效提升图像的细节信息。
3. 降低计算复杂度： HWA 模块中只有一半的特征图需要进行注意力机制操作，从而降低计算复杂度。

Half Wavelet Attention 结构图：

2、代码实现

import torch
import torch.nn as nndef dwt_init(x):x01 = x[:, :, 0::2, :] / 2x02 = x[:, :, 1::2, :] / 2x1 = x01[:, :, :, 0::2]x2 = x02[:, :, :, 0::2]x3 = x01[:, :, :, 1::2]x4 = x02[:, :, :, 1::2]x_LL = x1 + x2 + x3 + x4x_HL = -x1 - x2 + x3 + x4x_LH = -x1 + x2 - x3 + x4x_HH = x1 - x2 - x3 + x4# print(x_HH[:, 0, :, :])return torch.cat((x_LL, x_HL, x_LH, x_HH), 1)def iwt_init(x):r = 2in_batch, in_channel, in_height, in_width = x.size()out_batch, out_channel, out_height, out_width = in_batch, int(in_channel / (r ** 2)), r * in_height, r * in_widthx1 = x[:, 0:out_channel, :, :] / 2x2 = x[:, out_channel:out_channel * 2, :, :] / 2x3 = x[:, out_channel * 2:out_channel * 3, :, :] / 2x4 = x[:, out_channel * 3:out_channel * 4, :, :] / 2h = torch.zeros([out_batch, out_channel, out_height, out_width]).cuda() #h[:, :, 0::2, 0::2] = x1 - x2 - x3 + x4h[:, :, 1::2, 0::2] = x1 - x2 + x3 - x4h[:, :, 0::2, 1::2] = x1 + x2 - x3 - x4h[:, :, 1::2, 1::2] = x1 + x2 + x3 + x4return hclass DWT(nn.Module):def __init__(self):super(DWT, self).__init__()self.requires_grad = Truedef forward(self, x):return dwt_init(x)class IWT(nn.Module):def __init__(self):super(IWT, self).__init__()self.requires_grad = Truedef forward(self, x):return iwt_init(x)def conv(in_channels, out_channels, kernel_size, bias=False, stride=1):return nn.Conv2d(in_channels, out_channels, kernel_size,padding=(kernel_size // 2), bias=bias, stride=stride)class SALayer(nn.Module):def __init__(self, kernel_size=5, bias=False):super(SALayer, self).__init__()self.conv_du = nn.Sequential(nn.Conv2d(2, 1, kernel_size=kernel_size, stride=1, padding=(kernel_size - 1) // 2, bias=bias),nn.Sigmoid())def forward(self, x):# torch.max will output 2 things, and we want the 1st onemax_pool, _ = torch.max(x, dim=1, keepdim=True)avg_pool = torch.mean(x, 1, keepdim=True)channel_pool = torch.cat([max_pool, avg_pool], dim=1)  # [N,2,H,W]  could add 1x1 conv -> [N,3,H,W]y = self.conv_du(channel_pool)return x * yclass CALayer(nn.Module):def __init__(self, channel, reduction=16, bias=False):super(CALayer, self).__init__()# global average pooling: feature --> pointself.avg_pool = nn.AdaptiveAvgPool2d(1)# feature channel downscale and upscale --> channel weightself.conv_du = nn.Sequential(nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=bias),nn.ReLU(inplace=True),nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=bias),nn.Sigmoid())def forward(self, x):y = self.avg_pool(x)y = self.conv_du(y)return x * yclass HWB(nn.Module):def __init__(self, n_feat, o_feat, kernel_size=3, reduction=16, bias=False, act=nn.ReLU()):super(HWB, self).__init__()self.dwt = DWT()self.iwt = IWT()modules_body = \[conv(n_feat*2, n_feat, kernel_size, bias=bias),act,conv(n_feat, n_feat*2, kernel_size, bias=bias)]self.body = nn.Sequential(*modules_body)self.WSA = SALayer()self.WCA = CALayer(n_feat*2, reduction, bias=bias)self.conv1x1 = nn.Conv2d(n_feat*4, n_feat*2, kernel_size=1, bias=bias)self.conv3x3 = nn.Conv2d(n_feat, o_feat, kernel_size=3, padding=1, bias=bias)self.activate = actself.conv1x1_final = nn.Conv2d(n_feat, o_feat, kernel_size=1, bias=bias)def forward(self, x):residual = x# Split 2 partwavelet_path_in, identity_path = torch.chunk(x, 2, dim=1)# Wavelet domain (Dual attention)x_dwt = self.dwt(wavelet_path_in)res = self.body(x_dwt)branch_sa = self.WSA(res)branch_ca = self.WCA(res)res = torch.cat([branch_sa, branch_ca], dim=1)res = self.conv1x1(res) + x_dwtwavelet_path = self.iwt(res)out = torch.cat([wavelet_path, identity_path], dim=1)out = self.activate(self.conv3x3(out))out += self.conv1x1_final(residual)return outif __name__ == '__main__':x = torch.randn(1, 64, 128, 128).cuda()model = HWB(64, 64).cuda()output = model(x)print(output.shape)