## 3 Method
### Spatial-Channel Coordinated Attention Mechanism (SCCA)
#### 3.2.2 Implementation Details  
The implementation of the SCCA mechanism adopts a modular design to achieve a balance between computational efficiency and expressive power. Algorithm A2 (Appendix B) provides the complete pseudocode, and, below, we describe the key implementation details. **Module Architecture. ** SCCA contains four main components. * **Frequency Matrix Generator**: Extract high-frequency features using a \(3\times 3\) Sobel filter. Capture low-frequency components with a \(5\times 5\) Gaussian filter (\(\sigma=1.5\)). Calculate global statistics through second-order moment estimation. * **Channel Attention Path**: The multi-layer perceptron has a structure of \([C,C/2,C]\), where \(C\) is the channel dimension. The spectral gating unit uses the tanh activation function to implement high-frequency/low-frequency gating. Estimate the SNR using normalized median statistics. * **Spatial Attention Path**: The LF branch uses \(2\times\) downsampling followed by a \(3\times 3\) convolution. The HF branch uses a \(5\times 5\) median filter in the residual connection. A learnable temperature parameter \(\tau\) adjusts the focus of the softmax. * **Dual-Domain Projector**: Mix spatial-frequency domain transformations. The frequency-domain weights \(W_{freq}\) are implemented as a \(2D\) learnable mask. Separate spatial convolution and frequency operations, followed by additive fusion. **Parameter Initialization and Tuning. ** The effectiveness of SCCA highly depends on an appropriate initialization strategy. The channel MLP weights are initialized using Kaiming initialization, which is biased towards preserving information. The spatial attention filters are initialized using the Xavier initialization to improve the flow of gradients. The initial parameters of the spectral gating unit are \(\alpha_{base}=0.8\), \(\beta_{base}=0.3\). The learnable temperature \(\tau\) is initialized to 1.0 and restricted to the range of \([0.5,10]\). We have found the following tuning strategies to be crucial in achieving optimal performance: apply warmup in the first 25 epochs to gradually increase the influence of attention; apply dynamic learning rate scaling according to the estimated image illuminance; apply progressive attention training for particularly dark samples (<0.5 lux). **Computational Optimization. ** To maintain real-time performance, we have implemented the following optimizations. We calculate the LF spatial attention on the downsampled features, reducing the computation by 25%. We use the Fast Fourier Transform (FFT) to calculate the frequency-domain transformation. We apply channel pruning to remove channels that contribute less than 0.1% to the final performance. We use 16-bit floating-point precision with almost no impact on the accuracy (<0.2% drop). These optimizations enable the SCCA module to process input features with latency of less than 0.5 ms (on an NVIDIA A800 GPU), maintaining the real-time performance of the system. **Anti-Noise Strategy. ** To improve the robustness under extreme low-light conditions, we have adopted several specialized anti-noise strategies. Adaptive Thresholding: We dynamically adjust the noise threshold based on the SNR estimated for each frame. Correlation Guidance: We use correlation measurements to bias the preservation of high-frequency structural components. Gradient Calibration: We scale the gradients in accordance with Theorem 2. to ensure a balance between attention and denoising. Together, these strategies enable SCCA to effectively suppress noise while preserving details, reducing high-frequency hallucinations by 37% under conditions of <1 lux. In particular, our parameter phase alignment is verified by the gradient direction alignment coefficient \(\rho_{G}=0.93\), indicating the strong coherence between frequency-domain and spatial processing. The key difference between SCCA and previous attention mechanisms lies in its frequency-aware characteristics, allowing low-frequency semantic information to guide the processing of high-frequency details.