实验笔记之——Residual Dense Network for Image Super-Resolution (RDN)

最新推荐文章于 2025-05-05 11:38:29 发布

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最新推荐文章于 2025-05-05 11:38:29 发布

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本文链接：https://blog.youkuaiyun.com/gwplovekimi/article/details/98593459

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本文深入探讨了RDN（Residual Dense Network）在图像超分辨率领域的应用，详细解析了其核心模块——残差密集块（RDB）的设计原理与实现代码。RDN通过密集连接和残差学习有效利用了低分辨率图像的层次特征，提高了超分辨率效果。

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论文链接：https://arxiv.org/pdf/1802.08797.pdf

代码链接：https://github.com/yulunzhang/RDN


python train.py -opt options/train/train_sr.json
python3 test.py -opt options/test/test_sr.json



source activate pytorch

tensorboard --logdir tb_logger/ --port 6008


http://172.20.36.203:6008/#scalars

理论

本博文主要为代码复现，所以先简单给出RDN的介绍

Dense block就是为了make full use of the hierarchical features from the original low-resolution (LR) images。通过建立更加多的跳跃链接，使得feature map之间的更深层次的特征被映射出来。

As the network depth grows, the features in each convolutional layer would be hierarchical with different receptive fields（具有不同的接受域的层次结构）作者认为，随着网络深度的增加，每个CONV层的feature具有不同接受域的层次结构。因此不能更加充分的利用信息

RD block其实就是residual block和dense block的结合版本

Contiguous memory mechanism is realized by passing the state of preceding RDB to each layer of current RDB.将前一个传递到下一个~~~就是CM机制。

实验

Residual dense block (RDB)的复现代码如下：

class ResidualDenseBlock_5C(nn.Module):
    '''
    Residual Dense Block
    style: 5 convs
    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
    '''

    def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', \
            norm_type=None, act_type='leakyrelu', mode='CNA'):
        super(ResidualDenseBlock_5C, self).__init__()
        # gc: growth channel, i.e. intermediate channels
        self.conv1 = conv_block(nc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        self.conv2 = conv_block(nc+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        self.conv3 = conv_block(nc+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        self.conv4 = conv_block(nc+3*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        if mode == 'CNA':
            last_act = None
        else:
            last_act = act_type
        self.conv5 = conv_block(nc+4*gc, nc, 3, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=last_act, mode=mode)

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(torch.cat((x, x1), 1))
        x3 = self.conv3(torch.cat((x, x1, x2), 1))
        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        return x5.mul(0.2) + x

class ResidualDenseBlockTiny_4C(nn.Module):
    '''
    Residual Dense Block
    style: 4 convs
    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
    '''

    def __init__(self, nc, kernel_size=3, gc=16, stride=1, bias=True, pad_type='zero', \
            norm_type=None, act_type='leakyrelu', mode='CNA'):
        super(ResidualDenseBlockTiny_4C, self).__init__()
        # gc: growth channel, i.e. intermediate channels
        self.conv1 = conv_block(nc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        self.conv2 = conv_block(nc+gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        self.conv3 = conv_block(nc+2*gc, gc, kernel_size, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=act_type, mode=mode)
        if mode == 'CNA':
            last_act = None
        else:
            last_act = act_type
        self.conv4 = conv_block(nc+3*gc, nc, 3, stride, bias=bias, pad_type=pad_type, \
            norm_type=norm_type, act_type=last_act, mode=mode)

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(torch.cat((x, x1), 1))
        x3 = self.conv3(torch.cat((x, x1, x2), 1))
        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
        return x4.mul(0.2) + x