Atlases constructed using diffusion-weighted imaging (DWI) are important tools for studying human brain development. Atlas construction is in general a two-step process involving image registration and image fusion. The focus of most studies so far has been on improving registration thus image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases. In this paper, we propose a patch-based method for DWI atlas construction. Unlike other atlases that are based on the diffusion tensor model, our atlas is model-free. Instead of generating an atlas for each gradient direction independently and hence neglecting inter-image correlation, we propose to construct the atlas by jointly considering diffusion-weighted images of neighboring gradient directions. We employ a group regularization framework where local patches of angularly neighboring images are constrained for consistent spatio-angular atlas reconstruction. Experimental results verify that our atlas, constructed for neonatal data, reveals more structural details compared with the average atlas especially in the cortical regions. Our atlas also yields greater accuracy when used for image normalization.