The network architecture of LRR-DeblurNet consists of three main components: a blur estimation network for predicting blur kernel from a single image or video frame; a motion compensation module that leverages optical flow 不如... information to align consecutive frames and resolve temporal inconsistencies; and finally a restoration pathway that combines estimated blur kernel with aligned neighboring frames to reconstruct high-quality output images.
The Transformer Decoder Module:
def transformer_decoder:
# Self-attention across transformer blocks
att_scores = torch.matmul)
# Multi-head attention mechanism
head_att_scores = F.softmax, dim=-1)
# Weighted sum over value representations
context_vectors = torch.matmul
return context_vectors + query
This module demonstrates how attention mechanisms allow model to focus on relevant parts of input data dynamically. In practice, we observed that this architecture significantly improves temporal consistency by establishing long-range dependencies between frames.
多尺度特征融合流程图源：IEEE VIS Conf. '22
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# **四 章节划分建议** - 深度学习时代的技术演进
## **4.1 单阶段检测器设计**
### **特征金字塔多路径融合**
python
class FeaturePyramid:
def __init__:
super.__init__
self.p5_to_p4 = Conv(512//self.expansion, 256//self.expansion,
k=1) # Project p5 to p4 scale
self.p4_to_p3 = Conv(512//self.expansion if depth==6 else
...) # Handle different scales appropriately
### **解耦训练策略**
python
def separate_training_loop:
# Stage I - Blur Kernel Prediction only on syntic data
