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转载请标明出处：
https://dujinyang.blog.csdn.net/
本文出自:【奥特曼超人的博客】

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视频修复：从图像修复到动态场景的深度学习实践

1. 引言

随着视频内容的爆炸式增长（如监控录像、老电影数字化、虚拟现实内容），视频修复技术成为提升画质、延长内容生命周期的关键手段。

视频修复的目标包括：
• 去噪：消除摄像头抖动、压缩伪影、电子噪声。
• 超分辨率重建：从低分辨率视频恢复高清画面。
• 运动模糊修复：消除快速移动物体的模糊痕迹。
• 动态对象移除：修复被遮挡或侵入性物体（如水印、人物）。

传统方法（如中值滤波、插值法）效果有限，而深度学习通过建模复杂时空关系，显著提升了修复效果。

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2. 数据预处理

2.1 自定义数据集类

import cv2
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from torchvision.transforms import Compose, ToTensor, Resize, RandomAdjustBrightness

class VideoInpaintingDataset(Dataset):
    def __init__(self, video_path, mask_path=None, transform=None):
        self.video = cv2.VideoCapture(video_path)
        self.masks = None
        if mask_path:
            self.mask_video = cv2.VideoCapture(mask_path)
        
        self.transform = transform
    
    def __len__(self):
        return int(self.video.get(cv2.CAP_PROP_FRAME_COUNT))
    
    def __getitem__(self, idx):
        success, frame = self.video.read()
        if not success:
            raise StopIteration
        
        if self.masks:
            success_mask, mask = self.mask_video.read()
            if not success_mask:
                mask = np.zeros_like(frame, dtype=np.uint8)
            # 随机遮挡区域dujinyang
            h, w = frame.shape[:2]
            x = np.random.randint(0, w//2)
            y = np.random.randint(0, h//2)
            mask[y:y+h//2, x:x+w//2] = 255
            frame = frame * (1 - mask/255)
            # 添加噪声
            noise = np.random.normal(0, 25, frame.shape).astype(np.uint8)
            frame = cv2.add(frame, noise)
        
        if self.transform:
            frame = self.transform(frame)
        
        return frame

2.2 数据增强与加载

transform = Compose([
    Resize((256, 256)),
    RandomAdjustBrightness(0.2),
    ToTensor(),
    lambda x: x * 2 - 1  # 归一化到 [-1, 1]
])
# 数据处理部分-dujinyang
dataset = VideoInpaintingDataset(
    video_path="data/input_video.mp4",
    mask_path="data/mask_video.mp4",
    transform=transform
)
dataloader = DataLoader(dataset, batch_size=4, shuffle=True)

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3. 模型定义

3.1 空间修复网络（EDSR）

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

class EDSR(nn.Module):
    def __init__(self, scale_factor=4):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.res_blocks = nn.Sequential(*[
            nn.Sequential(
                nn.Conv2d(64, 64, kernel_size=3, padding=1),
                nn.ReLU(),
                nn.Conv2d(64, 64, kernel_size=3, padding=1)
            ) for _ in range(16)
        ])
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.upsample = nn.Sequential(
            nn.Conv2d(64, 64 * (scale_factor**2), kernel_size=3, padding=1),
            nn.PixelShuffle(scale_factor),
            nn.Conv2d(64, 3, kernel_size=3, padding=1)
        )
    
    def forward(self, x):
        residual = x
        x = F.relu(self.conv1(x))
        x = self.res_blocks(x)
        x = self.conv2(x)
        x += residual
        x = self.upsample(x)
        return x

3.2 时序建模网络（3D CNN）

class TemporalModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv3d = nn.Sequential(
            nn.Conv3d(3, 64, kernel_size=(3, 3, 3), padding=1),
            nn.ReLU(),
            nn.Conv3d(64, 64, kernel_size=(3, 3, 3), padding=1),
            nn.ReLU(),
            nn.Conv3d(64, 3, kernel_size=(3, 3, 3), padding=1)
        )
    
    def forward(self, x):
        x = x.permute(0, 2, 1, 3, 4)  #dujinyang- 调整维度为 (B, C, T, H, W)
        x = self.conv3d(x)
        return x

3.3 完整修复网络

class VideoRestorationNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.spatial_net = EDSR()
        self.temporal_net = TemporalModel()
    
    def forward(self, frames):
        spatial_out = []
        for t in range(frames.shape[1]):
            spatial_out.append(self.spatial_net(frames[:, t]))
        spatial_out = torch.stack(spatial_out, dim=1)
        temporal_out = self.temporal_net(spatial_out)
        return temporal_out

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4. 训练流程

4.1 损失函数与优化器

import torch.optim as optim

model = VideoRestorationNet()
criterion = nn.MSELoss()
optimizer = optim.AdamW(model.parameters(), lr=1e-4, weight_decay=1e-5)

scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5)

4.2 训练循环

num_epochs = 50
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

for epoch in range(num_epochs):
    model.train()
    total_loss = 0.0
    
    for batch_idx, frames in enumerate(dataloader):
        frames = frames.to(device)
        restored = model(frames)
        loss = criterion(restored, frames)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
        total_loss += loss.item()
    
    avg_loss = total_loss / len(dataloader)
    print(f"Epoch {epoch+1}/{num_epochs}, Loss: {avg_loss:.4f}")
    scheduler.step(avg_loss)

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5. 实时推理优化

5.1 模型导出为ONNX

import torch.onnx

dummy_input = torch.randn(1, 5, 3, 256, 256).to(device)
torch.onnx.export(
    model,
    dummy_input,
    "video_restoration.onnx",
    opset_version=12,
    input_names=["input"],
    output_names=["output"]
)

5.2 使用TensorRT加速推理

import tensorrt as trt

def build_engine(onnx_path):
    TRT_LOGGER = trt.Logger(trt.Logger.INFO)
    builder = trt.Builder(TRT_LOGGER)
    network = builder.create_network()
    
    parser = trt.OnnxParser(network, TRT_LOGGER)
    with open(onnx_path, 'rb') as model:
        if not parser.parse(model.read()):
            print("Failed to parse ONNX model")
            return None
    
    engine = builder.build_cuda_engine(network)
    return engine

def infer(engine, input_data):
    context = engine.create_execution_context()
    inputs, outputs, bindings, stream = allocate_buffers(engine)
    inputs[0].host = input_data.astype(np.float32)
    
    trt_outputs = do_inference(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
    return trt_outputs[0].reshape(-1, 3, 256, 256)

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6. 完整项目结构

video_restoration/
├── data/
│   ├── input_video.mp4
│   └── mask_video.mp4
├── models/
│   ├── edsr.py
│   └── temporal_model.py
├── train.py
├── infer.py
└── requirements.txt

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7. 关键优化技巧

1. 混合精度训练：使用 torch.cuda.amp 加速训练。
2. 帧采样策略：随机采样连续帧或关键帧。
3. 边缘填充：处理视频边缘的无效区域。
4. 多GPU训练：使用 DataParallel 或 DistributedDataParallel。

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作者：奥特曼超人Dujinyang

来源：CSDN

原文：https://dujinyang.blog.csdn.net/

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