Current low-light light-field (LF) image enhancement algorithms tend to produce blurry results, for (1) loss of spatial details during enhancement and (2) inefficient exploitation of angular correlations, which helps to recover spatial details. Therefore, in this article, we propose a parallel multi-scale network (PMSNet), which attempts to (1) process features of different scales in parallel to aggregate the different contributions of multi-scale features at each layer, thus fully preserve spatial details, and (2) integrate multi-resolution 3D convolution streams to efficiently utilize angular correlations. Specifically, PMSNet consists of three stages: Stage-I employs multi-scale modules (MSMs) to generate local understanding with the aid of adjacent views. Notably, MSM retains high-resolution feature extraction to minimize loss of spatial details. Stage-II processes all views to encode global information. Based on the above extracted local and global information, Stage-III utilizes 3D multi-scale modules (3D-MSMs) to efficiently exploit angular correlations. To validate our idea, we comprehensively evaluate the performance of PMSNet on three publicly available datasets. Experimental results show that our method is superior to the current state-of-the-art methods, achieving an average PSNR of 24.76 dB.