针对超分辨率任务中高频细节易失和跨维度特征交互不足的难题，提出一种融合动态门控机制与频谱约束的协同增强网络。该方法通过边缘保持增强、频域信息利用和通道-空间特征融合进行突破。首先设计边缘感知上采样模块，通过并行Sobel卷积提取边缘梯度特征，结合动态注意力机制增强纹理区域的高频表达，实现边缘引导的特征增强。然后，构建动态门控协同网络，引入多尺度门控卷积单元自适应调节跨通道特征交互，结合空间-频谱联合编码策略，通过动态权重分配强化局部细节与全局上下文的信息融合。最后，提出混合频谱约束损失函数，对频域快速傅里叶变换（fast Fourier transform, FFT）变换的实部与虚部分别施加L1约束，并联合边缘一致性损失构建多维度监督机制，有效优化高频成分重建。在Set5、Set14、BSD100、Urban100和Manga109五个基准数据集上，所提方法与主流方法在不同比例因子的任务当中进行了定性和定量比较，均取得了最优或次优的实验结果，同时可视化结果显示重建图像在边缘和周期性纹理的恢复效果显著优于现有方法。

The challenges of high-frequency detail loss and insufficient cross-dimensional feature interaction in super-resolution tasks were addressed through a collaborative enhancement network integrating dynamic gating mechanisms with spectral constraints. Significant improvements were achieved through three key components: edge-preserving enhancement, frequency-domain information utilization, and channel-spatial feature fusion. Edge gradient features were extracted via parallel Sobel convolutions in an edge-aware upsampling module, where high-frequency texture representation was enhanced by a dynamic attention mechanism. A dynamic gating collaborative network was constructed with multi-scale gated convolutional units to adaptively regulate cross-channel feature interactions. Spatial-spectral joint encoding was implemented through dynamic weight allocation to strengthen local-global information fusion. A hybrid spectral constraint loss function was proposed, where L1 constraints were separately applied to real and imaginary components of frequency-domain FFT(fast Fourier transforms). A multi-dimensional supervision framework was established through joint optimization with an edge consistency term. State-of-the-art quantitative results across multiple scaling factors were demonstrated on five benchmark datasets (Set5, Set14, BSD100, Urban100, Manga109). Superior restoration of sharp edges and periodic textures is confirmed through visual comparisons with existing methods.