The current light field super-resolution methods mainly face the following challenges: difficulty in handling redundant information in light fields; heavy reliance on the spatial domain to recover details; and insufficient interaction of spatial and angular features. We propose a novel light field super-resolution (LF-SR) network, termed DHSFNet, which effectively enhances super-resolution performance from a dual-domain perspective, encompassing both the frequency and spatial domains. Our DHSFNet contains three key points. (1) A local sparse angular attention module (LSAA) is proposed to selectively capture relationships between adjacent sub-views using geometric prior information to reduce computational complexity. (2) We design a dual-domain high-frequency restoration sub-network, with a frequency-domain branch using mask-guided multi-scale discrete cosine transform (DCT) restoration and a spatial-domain branch employing multi-scale cross-attention to recover texture details. (3) A Mamba-based fusion module (MF) is introduced to efficiently facilitate global spatial–angular interaction, which achieves linear complexity and outperforms Transformer-based methods in both accuracy and speed. Comprehensive experiments conducted on three benchmark datasets demonstrate the superior performance of our method in the LF-SR task.