The accurate estimation of the state of health (SOH) is crucial for effective battery management systems. This paper proposes a deep learning model dimension-wise convolutions-globalfilter networks (DimConv-GFNet) for lithium-ion battery SOH estimation. Particularly, the DimConv-GFNet comprises the dimension-wise convolutions (DimConv), which collect the multi-scale local features from different sensor signals, and lightweight global filter networks (GFNet) to capture long-range dependencies in the Fourier frequency domain. Unlike Transformer attention architectures, GFNet utilizes spectral properties to facilitate global information exchange with a lower computational complexity. Experiments on two datasets with a total of 167 commercial LFP/graphite cells validate the effectiveness of DimConv-GFNet. Although the model shows slightly lower accuracy compared to the DimConv-Transformer baseline, it delivers competitive performance with a root mean squared error (RMSE) of 0.335%, mean absolute error (MAE) of 0.233% and a mean absolute percentage error (MAPE) of 0.230%. Remarkably, the DimConv-GFNet substantially reduces computational demands, requiring fewer than one-third of the Floating Point Operations (FLOPs) and parameters of DimConv-Transformer. These results demonstrate DimConv-GFNet strikes a good balance between accuracy and efficiency, positioning it as a promising solution for efficient and accurate SOH estimation in battery management applications.