超分辨率重建 | 2025 FIWHN：轻量级超分辨率 SOTA！基于“宽残差”与 Transformer 混合架构的高效网络（代码实践）

AI即插即用2026-01-14 9:03

论文名称：Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network

论文原文 (Paper) ：https://arxiv.org/abs/2212.14181
官方代码 (Code) ：https://github.com/IVIPLab/FIWHN

超分辨率重建 | 2025 FIWHN：轻量级超分辨率 SOTA！基于"宽残差"与 Transformer 混合架构的高效网络（论文精读）

摘要 ：

本文提取自 IEEE TMM 顶刊论文《Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network (FIWHN)》。针对轻量级网络中激活函数导致特征丢失 以及CNN缺乏全局建模能力 的痛点，通过复现论文核心代码，提供了两个超强的即插即用模块：WDIB（宽残差蒸馏交互块） 和 TransBlock（高效Transformer块）。代码已封装好，复制即可无缝嵌入YOLO、UNet或ResNet等网络中进行魔改。

目录

第一部分：模块原理与实战分析

1. 论文背景与解决的痛点

在计算机视觉任务（尤其是超分辨率、目标检测的小目标层）中，我们经常面临两个尴尬的处境：

  1. 特征"死"在了激活函数上：传统CNN中广泛使用的ReLU等激活函数会导致中间层特征信息的丢失，尤其是在网络层数加深时，很多细节纹理就没了 。

  2. 局部与全局的割裂：CNN擅长提取局部纹理，Transformer擅长抓全局关联。现有的混合网络要么是简单的串联，要么是并行后硬拼凑，两者缺乏深度的特征交互，导致伪影产生 。

2. 核心模块原理揭秘

为了解决上述问题，FIWHN提出了两个核心组件，我已将其提取为独立的PyTorch模块：

  • WDIB (Wide-residual Distillation Interaction Block):

  • 对应代码类名 ：MY

  • 原理：利用"宽残差"机制，在激活函数前扩展通道数，防止特征丢失。同时引入了"特征交互"和"蒸馏"机制，通过学习系数将不同层级的特征进行加权融合 。

  • 作用：极大增强了网络对局部细节的保留能力。

  • TransBlock (Efficient Transformer):

  • 对应代码类名 ：TransBlock (包含 EffAttention)

  • 原理：为了解决Transformer计算量大的问题，采用了高效注意力机制（Efficient Attention），通过分组和分割操作降低显存占用，同时捕捉长距离依赖 。

  • 作用：弥补CNN全局感受野不足的缺陷。

3. 架构图解

建议参考论文中的 Figure 3 ，它详细展示了 WDIB 的内部构造（如何进行宽残差连接和蒸馏）以及 Efficient Transformer 的设计。

4. 适用场景与魔改建议

这套代码非常适合用于以下场景的改进：

  • YOLO系列的主干或Neck部分 ：用 WDIB 替换原有的 C2f 或 Bottleneck，增强特征提取能力。
  • 图像复原/超分任务：直接作为深层特征提取器。
  • 小目标检测 ：利用 TransBlock 增强全局上下文信息，防止小目标漏检。

第二部分：核心完整代码

博主提示 ：以下代码包含完整的辅助函数、核心模块（WDIB/MY 和 TransBlock）以及测试主函数。代码基于 PyTorch 实现，复制粘贴保存为 .py 文件即可运行。

python 复制代码 
"""
FIWHN 核心即插即用模块
提取自 FIWHN-基于特征交互加权混合网络的高效图像超分辨率

🔥 核心创新模块:
1. MY (WDIB): Wide-residual Distillation Interaction Block
   - 论文主要创新：特征交互和蒸馏机制
   - 实现了"特征交互加权"的核心思想
   
2. TransBlock: Efficient Transformer
   - 论文第二大创新：高效Transformer设计
   - CNN与Transformer的混合网络

📦 支撑模块:
- CoffConv: 系数卷积("加权"机制的关键)
- SRBW1, SRBW2: WDIB的构建块
- sa_layer: 空间-通道注意力
- EffAttention, Mlp: Transformer组件
- Scale: 可学习缩放因子

测试环境: anaconda torchv5
"""

import torch
import torch.nn as nn
import math
from torch.nn.parameter import Parameter


# ============================
# 辅助函数
# ============================

def std(x):
    """
    计算特征图的标准差
    
    参数:
        x: 输入特征图 (B, C, H, W)
    
    返回:
        标准差特征图 (B, C, 1, 1)
    """
    return torch.std(x, dim=[2, 3], keepdim=True)


def activation(act_type, inplace=False, neg_slope=0.05, n_prelu=1):
    """
    激活函数工厂
    
    参数:
        act_type: 激活函数类型 ('relu', 'lrelu', 'prelu')
        inplace: 是否in-place操作
        neg_slope: LeakyReLU的负斜率
        n_prelu: PReLU的参数数量
    
    返回:
        激活函数层
    """
    act_type = act_type.lower()
    if act_type == 'relu':
        layer = nn.ReLU()
    elif act_type == 'lrelu':
        layer = nn.LeakyReLU(neg_slope)
    elif act_type == 'prelu':
        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
    else:
        raise NotImplementedError('activation layer [{:s}] is not found'.format(act_type))
    return layer


def same_padding(images, ksizes, strides, rates):
    """
    计算same padding
    
    参数:
        images: 输入图像 (B, C, H, W)
        ksizes: 卷积核大小 [kh, kw]
        strides: 步长 [sh, sw]
        rates: 膨胀率 [rh, rw]
    
    返回:
        padding后的图像
    """
    assert len(images.size()) == 4
    batch_size, channel, rows, cols = images.size()
    out_rows = (rows + strides[0] - 1) // strides[0]
    out_cols = (cols + strides[1] - 1) // strides[1]
    effective_k_row = (ksizes[0] - 1) * rates[0] + 1
    effective_k_col = (ksizes[1] - 1) * rates[1] + 1
    padding_rows = max(0, (out_rows-1)*strides[0]+effective_k_row-rows)
    padding_cols = max(0, (out_cols-1)*strides[1]+effective_k_col-cols)
    
    padding_top = int(padding_rows / 2.)
    padding_left = int(padding_cols / 2.)
    padding_bottom = padding_rows - padding_top
    padding_right = padding_cols - padding_left
    paddings = (padding_left, padding_right, padding_top, padding_bottom)
    images = torch.nn.ZeroPad2d(paddings)(images)
    return images


def extract_image_patches(images, ksizes, strides, rates, padding='same'):
    """
    提取图像patches
    
    参数:
        images: 输入图像 (B, C, H, W)
        ksizes: patch大小 [kh, kw]
        strides: 步长 [sh, sw]
        rates: 膨胀率 [rh, rw]
        padding: padding类型 ('same' or 'valid')
    
    返回:
        patches (B, C*kh*kw, L), L是patch数量
    """
    assert len(images.size()) == 4
    assert padding in ['same', 'valid']
    
    if padding == 'same':
        images = same_padding(images, ksizes, strides, rates)
    elif padding == 'valid':
        pass
    else:
        raise NotImplementedError('Unsupported padding type: {}. Only "same" or "valid" are supported.'.format(padding))

    unfold = torch.nn.Unfold(kernel_size=ksizes,
                             dilation=rates,
                             padding=0,
                             stride=strides)
    patches = unfold(images)
    return patches


def reverse_patches(images, out_size, ksizes, strides, padding):
    """
    将patches重组为图像
    
    参数:
        images: patches (B, C*kh*kw, L)
        out_size: 输出图像尺寸 (H, W)
        ksizes: patch大小 [kh, kw]
        strides: 步长
        padding: padding大小
    
    返回:
        重组后的图像 (B, C, H, W)
    """
    unfold = torch.nn.Fold(output_size=out_size, 
                          kernel_size=ksizes, 
                          dilation=1, 
                          padding=padding, 
                          stride=strides)
    patches = unfold(images)
    return patches


# ============================
# 基础模块
# ============================

class Scale(nn.Module):
    """
    可学习的缩放因子
    
    参数:
        init_value: 初始缩放值
    """
    def __init__(self, init_value=1e-3):
        super().__init__()
        self.scale = nn.Parameter(torch.FloatTensor([init_value]))

    def forward(self, input):
        return input * self.scale





# ============================
# 注意力模块
# ============================

class sa_layer(nn.Module):
    """
    空间-通道混洗注意力层
    
    参数:
        n_feats: 特征通道数
        groups: 分组数量(默认4)
    """
    def __init__(self, n_feats, groups=4):
        super(sa_layer, self).__init__()
        self.groups = groups
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.cweight = Parameter(torch.zeros(1, n_feats // (2 * groups), 1, 1))
        self.cbias = Parameter(torch.ones(1, n_feats // (2 * groups), 1, 1))
        self.sweight = Parameter(torch.zeros(1, n_feats // (2 * groups), 1, 1))
        self.sbias = Parameter(torch.ones(1, n_feats // (2 * groups), 1, 1))

        self.sigmoid = nn.Sigmoid()
        self.gn = nn.GroupNorm(n_feats // (2 * groups), n_feats // (2 * groups))

    @staticmethod
    def channel_shuffle(x, groups):
        """通道混洗"""
        b, c, h, w = x.shape
        x = x.reshape(b, groups, -1, h, w)
        x = x.permute(0, 2, 1, 3, 4)
        x = x.reshape(b, -1, h, w)
        return x

    def forward(self, x):
        b, c, h, w = x.shape

        x = x.reshape(b * self.groups, -1, h, w)
        x_0, x_1 = x.chunk(2, dim=1)

        # channel attention
        xn = self.avg_pool(x_0)
        xn = self.cweight * xn + self.cbias
        xn = x_0 * self.sigmoid(xn)

        # spatial attention
        xs = self.gn(x_1)
        xs = self.sweight * xs + self.sbias
        xs = x_1 * self.sigmoid(xs)

        # concatenate along channel axis
        out = torch.cat([xn, xs], dim=1)
        out = out.reshape(b, -1, h, w)

        out = self.channel_shuffle(out, 2)
        return out


class CoffConv(nn.Module):
    """
    系数卷积 - 结合均值池化和标准差分支
    
    参数:
        n_feats: 特征通道数
    """
    def __init__(self, n_feats):
        super(CoffConv, self).__init__()
        # 上分支: 均值池化
        self.upper_branch = nn.Sequential(
            nn.AdaptiveAvgPool2d(1),
            nn.Conv2d(n_feats, n_feats // 8, 1, 1, 0),
            nn.ReLU(inplace=True),
            nn.Conv2d(n_feats // 8, n_feats, 1, 1, 0),
            nn.ReLU(inplace=True),
            nn.Sigmoid()
        )

        self.std = std
        # 下分支: 标准差
        self.lower_branch = nn.Sequential(
            nn.Conv2d(n_feats, n_feats // 8, 1, 1, 0),
            nn.ReLU(inplace=True),
            nn.Conv2d(n_feats // 8, n_feats, 1, 1, 0),
            nn.ReLU(inplace=True),
            nn.Sigmoid()
        )

    def forward(self, fea):
        upper = self.upper_branch(fea)
        lower = self.std(fea)
        lower = self.lower_branch(lower)
        out = torch.add(upper, lower) / 2
        return out


# ============================
# 残差块模块
# ============================

class SRBW1(nn.Module):
    """
    简单残差块带权重1 (Simple Residual Block with Weight 1)
    
    参数:
        n_feats: 特征通道数
        wn: 权重归一化函数
        act: 激活函数
    """
    def __init__(self, n_feats, wn=lambda x: torch.nn.utils.weight_norm(x), act=nn.ReLU(True)):
        super(SRBW1, self).__init__()
        self.res_scale = Scale(1)
        self.x_scale = Scale(1)
        body = []
        body.append(nn.Conv2d(n_feats, n_feats*2, kernel_size=1, padding=0))
        body.append(act)
        body.append(nn.Conv2d(n_feats*2, n_feats//2, kernel_size=1, padding=0))
        body.append(nn.Conv2d(n_feats//2, n_feats, kernel_size=3, padding=1))

        self.body = nn.Sequential(*body)
        self.SAlayer = sa_layer(n_feats)

    def forward(self, x):
        y = self.res_scale(self.body(x))
        return y


class SRBW2(nn.Module):
    """
    简单残差块带权重2 (Simple Residual Block with Weight 2)
    
    参数:
        n_feats: 特征通道数
        wn: 权重归一化函数
        act: 激活函数
    """
    def __init__(self, n_feats, wn=lambda x: torch.nn.utils.weight_norm(x), act=nn.ReLU(True)):
        super(SRBW2, self).__init__()
        self.res_scale = Scale(1)
        self.x_scale = Scale(1)
        body = []
        body.append(nn.Conv2d(n_feats, n_feats*2, kernel_size=1, padding=0))
        body.append(act)
        body.append(nn.Conv2d(n_feats*2, n_feats//2, kernel_size=1, padding=0))
        body.append(nn.Conv2d(n_feats//2, n_feats//2, kernel_size=3, padding=1))

        self.body = nn.Sequential(*body)
        self.SAlayer = sa_layer(n_feats//2)
        self.conv = nn.Conv2d(n_feats, n_feats//2, kernel_size=3, padding=1)

    def forward(self, x):
        y = self.res_scale(self.body(x)) + self.x_scale(self.conv(x))
        return y


# ============================
# 交互蒸馏块
# ============================

class MY(nn.Module):
    """
    主要交互蒸馏块 (Main interactYon block)
    这是WDIB的核心实现,包含多个SRBW块和CoffConv系数卷积
    
    参数:
        n_feats: 特征通道数
        act: 激活函数
    """
    def __init__(self, n_feats, act=nn.ReLU(True)):
        super(MY, self).__init__()

        self.act = activation('lrelu', neg_slope=0.05)
        wn = lambda x: torch.nn.utils.weight_norm(x)
        self.srb1 = SRBW1(n_feats)
        self.srb2 = SRBW1(n_feats)
        self.rb1 = SRBW1(n_feats)
        self.rb2 = SRBW1(n_feats)
        self.A1_coffconv = CoffConv(n_feats)
        self.B1_coffconv = CoffConv(n_feats)
        self.A2_coffconv = CoffConv(n_feats)
        self.B2_coffconv = CoffConv(n_feats)
        self.conv_distilled1 = nn.Conv2d(n_feats, n_feats, kernel_size=1, stride=1, padding=0, bias=False)
        self.conv_distilled2 = nn.Conv2d(n_feats, n_feats, kernel_size=1, stride=1, padding=0, bias=False)
        self.sigmoid1 = nn.Sigmoid()
        self.sigmoid2 = nn.Sigmoid()
        self.sigmoid3 = nn.Sigmoid()
        self.scale_x1 = Scale(1)
        self.scale_x2 = Scale(1)
        self.srb3 = SRBW1(n_feats)
        self.srb4 = SRBW1(n_feats)
        self.fuse1 = SRBW2(n_feats*2)
        self.fuse2 = nn.Conv2d(2*n_feats, n_feats, kernel_size=1, stride=1, padding=0, bias=False, dilation=1)

    def forward(self, x):
        out_a = self.act(self.srb1(x))
        distilled_a1 = remaining_a1 = out_a
        out_a = self.rb1(remaining_a1)
        A1 = self.A1_coffconv(out_a)
        out_b_1 = A1 * out_a + x
        B1 = self.B1_coffconv(x)
        out_a_1 = B1 * x + out_a

        out_b = self.act(self.srb2(out_b_1))
        distilled_b1 = remaining_b1 = out_b
        out_b = self.rb2(remaining_b1)
        A2 = self.A2_coffconv(out_a_1)
        out_b_2 = A2 * out_a_1 + out_b
        out_b_2 = out_b_2 * self.sigmoid1(self.conv_distilled1(distilled_b1))
        B2 = self.B2_coffconv(out_b)
        out_a_2 = out_b * B2 + out_a_1
        out_a_2 = out_a_2 * self.sigmoid2(self.conv_distilled2(distilled_a1))

        out_a_out = self.srb3(out_a_2)
        out_b_out = self.srb4(out_b_2)

        out1 = self.fuse1(torch.cat([self.scale_x1(out_a_out), self.scale_x2(out_b_out)], dim=1))
        out2 = self.sigmoid3(self.fuse2(torch.cat([self.scale_x1(out_a_out), self.scale_x2(out_b_out)], dim=1)))
        
        out = out2 * out_b_out
        y1 = out1 + out

        return y1


# ============================
# Transformer模块
# ============================

class Mlp(nn.Module):
    """
    多层感知器 (MLP)
    
    参数:
        in_features: 输入特征维度
        hidden_features: 隐藏层特征维度
        out_features: 输出特征维度
        act_layer: 激活函数层
        drop: dropout率
    """
    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, drop=0.):
        super().__init__()
        out_features = out_features or in_features
        hidden_features = hidden_features or in_features//4
        self.fc1 = nn.Linear(in_features, hidden_features)
        self.act = act_layer()
        self.fc2 = nn.Linear(hidden_features, out_features)
        self.drop = nn.Dropout(drop)

    def forward(self, x):
        x = self.fc1(x)
        x = self.act(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.drop(x)
        return x


class EffAttention(nn.Module):
    """
    高效注意力机制 (Efficient Attention)
    使用分组注意力机制降低计算复杂度
    
    参数:
        dim: 特征维度
        num_heads: 注意力头数
        qkv_bias: 是否使用QKV的bias
        qk_scale: QK缩放因子
        attn_drop: 注意力dropout率
        proj_drop: 投影dropout率
    """
    def __init__(self, dim, num_heads=9, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
        super().__init__()
        self.num_heads = num_heads
        head_dim = dim // num_heads
        self.scale = qk_scale or head_dim ** -0.5

        self.reduce = nn.Linear(dim, dim//2, bias=qkv_bias)
        self.qkv = nn.Linear(dim//2, dim//2 * 3, bias=qkv_bias)
        self.proj = nn.Linear(dim//2, dim)
        self.attn_drop = nn.Dropout(attn_drop)

    def forward(self, x):
        x = self.reduce(x)
        B, N, C = x.shape
        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        
        q_all = torch.split(q, math.ceil(N//4), dim=-2)
        k_all = torch.split(k, math.ceil(N//4), dim=-2)
        v_all = torch.split(v, math.ceil(N//4), dim=-2)

        output = []
        for q, k, v in zip(q_all, k_all, v_all):
            attn = (q @ k.transpose(-2, -1)) * self.scale
            attn = attn.softmax(dim=-1)
            attn = self.attn_drop(attn)
            trans_x = (attn @ v).transpose(1, 2)
            output.append(trans_x)
        x = torch.cat(output, dim=1)
        x = x.reshape(B, N, C)
        x = self.proj(x)
        return x


class TransBlock(nn.Module):
    """
    Transformer块 (Transformer Block)
    结合高效注意力和MLP
    
    参数:
        n_feat: 特征通道数
        dim: Transformer维度
        num_heads: 注意力头数
        mlp_ratio: MLP扩展比例
        qkv_bias: 是否使用QKV的bias
        qk_scale: QK缩放因子
        drop: dropout率
        attn_drop: 注意力dropout率
        drop_path: drop path率
        act_layer: 激活函数层
        norm_layer: 归一化层
    """
    def __init__(
        self, n_feat=64, dim=768, num_heads=8, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
        drop_path=0., act_layer=nn.ReLU, norm_layer=nn.LayerNorm):
        super(TransBlock, self).__init__()
        self.dim = dim
        self.atten = EffAttention(self.dim, num_heads=9, qkv_bias=False, qk_scale=None,
                             attn_drop=0., proj_drop=0.)
        self.norm1 = nn.LayerNorm(self.dim)
        self.mlp = Mlp(in_features=dim, hidden_features=dim//4, act_layer=act_layer, drop=drop)
        self.norm2 = nn.LayerNorm(self.dim)

    def forward(self, x):
        b, c, h, w = x.shape
        x = extract_image_patches(x, ksizes=[3, 3],
                                  strides=[1, 1],
                                  rates=[1, 1],
                                  padding='same')
        x = x.permute(0, 2, 1)
        x = x + self.atten(self.norm1(x))
        x = x + self.mlp(self.norm2(x))
        x = x.permute(0, 2, 1)
        x = reverse_patches(x, (h, w), (3, 3), 1, 1)
        
        return x


# ============================
# 测试代码
# ============================

if __name__ == "__main__":
    # 输入 B C H W, 输出 B C H W
    device = 'cuda' if torch.cuda.is_available() else 'cpu'
    x = torch.randn(1, 32, 64, 64).to(device)
    
    print("=" * 60)
    print("FIWHN 核心即插即用模块测试")
    print(f"运行设备: {device}")
    print("=" * 60)

    
    # 测试MY (WDIB核心模块) - 论文主要创新
    print("[核心1] MY - Wide-residual Distillation Interaction Block:")
    my_block = MY(32).to(device)
    y_my = my_block(x)
    print(f"  输入shape: {x.shape}")
    print(f"  输出shape: {y_my.shape}")
    print(f"  WDIB是本文的主要创新，实现特征交互和蒸馏机制")

    # 测试TransBlock - 论文第二大创新
    print("[核心2] TransBlock - Efficient Transformer:")
    trans_block = TransBlock(n_feat=32, dim=32*9).to(device)
    y_trans = trans_block(x)
    print(f"  输入shape: {x.shape}")
    print(f"  输出shape: {y_trans.shape}")
    print(f"  高效Transformer设计，CNN与Transformer混合")
    print()

第三部分：结果验证与总结

为了确保代码的可用性，我已经在本地环境（PyTorch + CUDA）进行了测试。运行上述代码的 __main__ 部分，你可以看到清晰的输入输出维度打印，证明模块可以跑通且不改变特征图尺寸（Padding处理得当），真正做到了"即插即用"。

总结 ：

FIWHN 通过 WDIB 解决了特征在深层网络中丢失的问题，又通过 TransBlock 以极低的计算成本引入了全局注意力。如果你正在做 YOLO 改进、语义分割或者图像复原，这两个模块绝对值得一试！

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