## 2 Dataset and Methodology
### Methodology
#### 2.2.2 Component Design  
1. Dual-Generator Architecture  
The framework deploys twin ResNet-based generators [35] (G: \(X\)\(\rightarrow\)\(Y\) and \(F\): \(Y\)\(\rightarrow\)\(X\)) with shared architectural principles but domain-specific parameterization. As shown in Figure 3, each generator follows a four-stage processing chain:a. Encoder Phase  
Input images undergo progressive downsampling through three CIR (Conv-InstanceNorm-ReLU) blocks:  
\[\text{CIR}(x)=\text{ReLU}\big{(}\text{InstanceNorm}(\text{Conv}_{3\times 3}(x)) \big{)}, \tag{1}\]  
where x represents the input feature map, and CIR(\(x\)) represents a standard convolution normalization activation unit, which constitutes the basic downsampling module in the encoding stage, where each convolution layer uses stride = 2 for spatial reduction, compressing 500 \(\times\) 500 inputs to 125 \(\times\) 125 feature maps with 256 channels. Reflection padding maintains edge continuity during downsampling. b. Transformation Core  
Four residual blocks process features without dimension alteration, implementing:  
\[\mathcal{R}(x)=x+\text{CIR}(\text{CIR}(x)), \tag{2}\]  
where R(x) represents the output of the residual block, and CIR(CIR(\(x\))) represents the features processed by two consecutive layers of CIR modules. By \(R(x)=x+\text{CIR}(\text{CIR}(x))\), the constant dimension mapping of features is achieved to retain semantic information, similar to the standard ResNet structure. This preserves critical road topology while filtering spectral noise through skip connections. c. EMAU Attention Module  
The exponential moving average unit (EMAU) replaces conventional self-attention through [36]:  
\[EMA(Q,K,V)=Softmax\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}}\bigg{)}\cdot V\cdot\gamma +x, \tag{3}\]  
where \(\gamma\) denotes learnable decay factors (initialized at 0.9) that adaptively weight historical feature states. EMAU replaces conventional self-attention. This design introduces a bottleneck structure that significantly reduces computational complexity. EMAU achieves comparable or better performance in feature representation quality while reducing the computational overhead by approximately 37% compared to standard self-attention mechanisms used in transformers. Compared to the non-local block, EMAU maintains similar accuracy with notably lower FLOPs and memory usage due to its efficient recursive update scheme. Figure 3: Architecture of the generator. d. Decoder Phase  
Two CTIR (ConvTranspose-InstanceNorm-ReLU) blocks perform upsampling:  
\[\text{CTIR}(x)=\text{ReLU}\big{(}\text{InstanceNorm}\big{(}\text{ConvTranspose}_{3 \times 3}(x)\big{)}\big{)}, \tag{4}\]  
and final reflection padding and convolution restore the output resolution to 500 \(\times\) 500, achieving 0.914 structural similarity with input images. (2) PatchGAN Discriminator  
The discriminators \(D_{X}/D_{Y}\) employ the multi-receptive-field PatchGAN architecture (Figure 4) with enhanced local detail discrimination [37]:  
a. Architecture of the generator  
Five convolutional layers (kernel sizes: 64-128-256-512-1) process 70 \(\times\) 70 image patches through:  
\[\mathcal{D}(x)=\sum_{i=1}^{5}LeakyReLU\Big{(}InstanceNorm\Big{(}Conv_{4\times 4 }^{(i)}(x)\Big{)}\Big{)}, \tag{5}\]  
where stride = 2 operations progressively halve spatial dimensions, with padding = 1 preserving feature map boundaries. Layer-specific configurations are:  
Layer 1: 64 filters, stride = 2 \(\rightarrow\) Output: 35 \(\times\) 35 \(\times\) 64  
Layer 2: 128 filters, stride = 2 \(\rightarrow\) Output: 17 \(\times\) 17 \(\times\) 128  
Layer 3: 256 filters, stride = 2 \(\rightarrow\) Output: 8 \(\times\) 8 \(\times\) 256  
Layer 4: 512 filters, stride = 1 \(\rightarrow\) Output: 8 \(\times\) 8 \(\times\) 512  
Layer 5: 1 filter, stride = 1 \(\rightarrow\) Output: 8 \(\times\) 8 \(\times\) 1. LeakyReLU activations (\(\alpha=0.2\)) prevent gradient saturation while maintaining negative signal propagation.