Light field cameras, capable of capturing both the intensity and direction of light, are utilized in applications like foreground occlusion removal and depth estimation. However, the limited size of imaging sensors restricts the simultaneous achievement of high spatial and angular resolution in light field images. This paper introduces a method combining Fourier convolution and channel attention for light field angular reconstruction, which indirectly creates dense light field images from sparse ones using reference views from the image's four corners. This method leverages the light field image's inherent 4D structure, employing channel⁃level dense fast Fourier residual convolution blocks to model the spatial and angular correlations in both spatial and frequency domains. Channel attention blocks, utilizing global response normalization, then adaptively fuse these channels. Furthermore, an enhanced viewpoint⁃weighted indirect synthesis approach is proposed, assigning a confidence map to each reference view to improve the synthesis of new, realistic views by establishing relationships between reference views. Experimental results demonstrate that our method outperforms the advanced light field angular reconstruction technique IRVAE, showing an average PSNR improvement of 0.08, 0.13, and 0.13 dB on the natural light field dataset 30Scenes, Occlusion, and Reflective, respectively, ensuring clear reconstruction results with angular consistency in the light field.