## Appendix C Supplementary Results  
### Algorithm  
```
Input :Multi-scale features \(\{F_{i}\}_{i=1}^{N}\) Output :Fused feature \(F_{fused}\)
1 FunctionFrequencyAdaptiveFusion.Initialize(in_channels_,num_scales):
2 learnable_wavelets \(\leftarrow\varnothing\);
3 for\(i=1\)to\(num\_scales\)do
4 learnable_wavelets.append(Conv2d(in_channels,in_channels,3));
5\(\alpha\leftarrow\) Parameter(ones(1)) ; // HF weight initialization
6\(\beta\leftarrow\) Parameter(ones(1)) ; // LF weight initialization
7 FunctionEstimatellumination(x):
8 intensity \(\leftarrow\) mean(x);
9 lux \(\leftarrow\) clamp(intensity \(\times\) 255.0, 0.1, 15.0);
10 returnlux;
11 FunctionAdaptiveDecomposition(x):
12 lux \(\leftarrow\) EstimateIllumination(x);
13\(\sigma_{t}\leftarrow\alpha\cdot e^{-\beta flux}\) ; // Adaptive threshold
14 HF_components, LF_components \(\leftarrow\varnothing,\varnothing\);
15 forachwavelet in learnable_waveletsdo
16 feature \(\leftarrow\) wavelet(x);
17 HF \(\leftarrow\) LoG(feature,\(\sigma_{t}\)) ; // High - frequency extraction
18 LF \(\leftarrow\) GaussianBlur(feature,\(\sigma_{t}\times 2\)) ; // Low - frequency extraction
19 HF_components.append(HF);
20 LF_components.append(LF);
21 returnHF_components, LF_components;
22
23 FunctionScaleFrequencyAttention(HF,LF):
24\(F_{base}\leftarrow\Gamma_{intra}(HF)\oplus\Gamma_{inter}(LF)\) ; // Base fusion
25 for\(s\in\{HF,MF,LF\}\)do
26\(A_{s}\leftarrow\frac{\exp(W_{s}F_{base}+b_{s})}{\sum_{i^{\prime}}\exp(W_{s}^{ \prime}+b_{s}^{\prime})}\) ; // Attention calculation
27 return\(A_{MF},A_{MF},A_{LF}\);
28
29 FunctionNonlinearBandFusion(HF,LF,A_{MF},A_{LF}\):
30 lux \(\leftarrow\) EstimatedIllumination(concat(HF,LF));
31 forachfeature in learnable_wavelets \(\leftarrow\varnothing\);
32
33 forachfeat in learnable_wavelets \(\leftarrow\varnothing\);
34\(\sigma_{t}\leftarrow\) Variance(ones(1)) ; // LF weight initialization
35\(\beta\leftarrow\) Parameter(ones(1)) ; // LF weight initialization
36
37 FunctionEstimatellumination(x):
38 intensity \(\leftarrow\) mean(x);
39 lux \(\leftarrow\) clamp(intensity \(\times\) 255.0, 0.1, 15.0);
40 returnlux;
41 FunctionAdaptiveDecomposition(x):
42\(\sigma_{t}\leftarrow\) EstimateIllumination(x);
53\(\sigma_{t}\leftarrow\)\(\alpha\cdot e^{-\beta flux}\) ; // Adaptive threshold
54\(\sigma_{t}\leftarrow\)\(\alpha\cdot e^{-\beta flux}\) ; // Adaptive threshold
55\(\sigma_{t}\leftarrow\)\(\alpha\cdot e^{-\beta flux}\) ; // Adaptive threshold
56\(\sigma_{t}\leftarrow\)\(\alpha\cdot e^{-\beta flux}\) ; // Adaptive threshold
57\(\sigma_{t}\leftarrow\)\(\alpha\cdot e^{-\beta flux}\) ; // Adaptive threshold
58\(\sigma_{t}\leftarrow\)\(\alpha\cdot e^{-\beta flux}\) ; // Adaptive threshold
59\(\sigma_{t}\leftarrow\)\(\alpha\cdot e^{-\


```
Input :Feature map \(X\in\mathbb{R}^{C\times H\times W}\) Output :Enhanced feature map \(F_{SCCA}\)
1 Class SCCA:
2 FunctionInitialize(in_channels):
3 sobel \(\leftarrow\) Conv2d(in_channels, in_channels, 3, groups = in_channels);
4 gaussian \(\leftarrow\) GaussianBlur(kernel_size = 5);
5 mlp \(\leftarrow\) Sequential(FC(C, C/2), ReLU, FC(C/2, C));
6 depth_conv \(\leftarrow\) DepthwiseConv(in_channels, in_channels, 3);
7 spectral_gate \(\leftarrow\) SpectralGating();
8 invariant_norm \(\leftarrow\) InvariantNorm();
9 FunctionComputeFrequencyMomentMatrix(X):
10\(M_{HF}\leftarrow\mathbb{E}[X*K_{ Sobel}]\) ; // High - frequency moment
11\(M_{LF}\leftarrow\mathbb{E}[X*G_{\sigma}]\) ; // Low - frequency moment
12 return\(M_{HF},M_{LF}\);
13 FunctionChannelAttention(X):
14\(M_{HF},M_{LF}\leftarrow\) ComputeFrequencyMomentMatrix(X);
15 gap \(\leftarrow\) GAP(\(M_{LF}\)) ; // Global average pooling
16 depth_feat \(\leftarrow\) depth_conv(\(M_{HF}\));
17 depth_feat \(\leftarrow\) GAP(depth_feat);
18 concat_feat \(\leftarrow\) [mlp(gap); depth_feat] ; // Feature concatenation
19\(A_{c}\leftarrow\)\(\Omega\)(concat_feat) ; // Spectral gating unit
20\(A_{c}\leftarrow\) reshape(\(A_{c}\), [B, C, 1, 1]) ; // Reshape for broadcasting
21 return\(A_{c}\);
22 FunctionSpatialAttention(X):
23 down_X \(\leftarrow\) AvgPool2d(2)(X) ; // Downsampling
24\(A_{s}^{LF}\leftarrow\sigma(W_{1}*down_{-}X)\) ; // LF spatial attention
25\(A_{s}^{LF}\leftarrow\) Upsample(2)(\(A_{s}^{LF}\)) ; // Original size
26 median_X \(\leftarrow\) MedianBlur(X);
27 diff_X \(\leftarrow\) X \(\odot\) median_X ; // HF residual extraction
28\(A_{s}^{HF}\leftarrow\) tanh(\(W_{2}*diff_{-}X\)) ; // HF spatial attention
29 weights \(\leftarrow\) LearnableSoftmax([\(A_{s}^{LF},A_{s}^{HF}\)] ; // Learnable weights
30\(A_{s}\leftarrow\) weights ; // Final spatial attention map
31 return\(A_{s}\);
32 FunctionPhaseAlignedFusion(X, \(A_{c}\), \(A_{s}\)):
33\(X_{channel}\leftarrow\) InvariantNorm(\(A_{c}\odot X\)) ; // Channel stream
34\(X_{spatial}\leftarrow\) D(\(A_{s}\)) \(\otimes\) X ; // Spatial stream
35 output \(\gets X_{channel}+X_{spatial}\) ; // Fusion
36 returnoutput;
37 FunctionDualDomainProjection(\(A_{s}\)):
38 D(\(A_{s}\)) \(\leftarrow\)\(W_{spatial}*A_{s}+\mathcal{F}^{-1}(W_{freq}\odot\mathcal{F}(A_{s}))\) ; // Dual - domain projection
39 returnD(\(A_{s}\));
40 FunctionForward(X):
41\(A_{c}\leftarrow\) ChannelAttention(X);
42\(A_{s}\leftarrow\) SpatialAttention(X);
43\(F_{SCCA}\leftarrow\) PhaseAlignedFusion(X, \(A_{c}\), \(A_{s}\));
44 return\(F_{SCCA}\);
```

**Algorithm A2**Spatial-Channel Coordinated Attention (SCCA).