dnn_superres

### DNN 超分辨率实现与技术

#### 深度学习模型用于图像超分辨率

深度卷积神经网络（DCNNs）已被广泛应用于提升低分辨率图像的质量。通过引入残差学习框架，可以有效解决深层网络训练中的梯度消失问题[^1]。

import torch.nn as nn

class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(channels)
        self.prelu = nn.PReLU()
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(channels)

    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.prelu(out)
        out = self.conv2(out)
        out = self.bn2(out)
        out += residual
        return out

#### 子像素卷积层的应用

子像素卷积层能够有效地将特征图放大到高分辨率空间，从而提高重建精度并减少计算量。该方法利用了高效的空间转换操作来替代传统的插值方式。

def pixel_shuffle(input, upscale_factor):
    batch_size, channels, in_height, in_width = input.size()
    channels //= upscale_factor ** 2
    
    out_height = in_height * upscale_factor
    out_width = in_width * upscale_factor
    
    input_view = input.contiguous().view(
        batch_size, channels, upscale_factor, upscale_factor,
        in_height, in_width
    )
    
    shuffle_out = input_view.permute(0, 1, 4, 2, 5, 3).contiguous()
    return shuffle_out.view(batch_size, channels, out_height, out_width)

#### 训练策略优化

为了获得更好的性能，在训练过程中通常会采用多种损失函数组合的方式来进行监督学习。除了常见的均方误差(MSE)，还可以加入感知损失(perceptual loss)以及对抗损失(adversarial loss)。

criterion_mse = nn.MSELoss()
criterion_perceptual = PerceptualLoss()  # 自定义或第三方库提供
criterion_adversarial = AdversarialLoss()

total_loss = criterion_mse(output, target) + \
             perceptual_weight * criterion_perceptual(output, target) + \
             adversarial_weight * criterion_adversarial(discriminator_output)