Novel view synthesis has achieved remarkable quality and efficiency by the paradigm of 3D Gaussian Splatting (3D-GS), but still faces two challenges: 1) significant performance degradation when trained with only few-shot samples due to a lack of geometry constraint, and 2) incapability of rendering at a higher resolution that is beyond the input resolution of training samples. In this paper, we propose Dual-Lens 3D-GS (DL-GS) to achieve high-resolution (HR) and few-shot view synthesis, by leveraging the characteristics of the asymmetric dual-lens system commonly equipped on mobile devices. This kind of system captures the same scene with different focal lengths (\textit{i.e.}, wide-angle and telephoto) under an asymmetric stereo configuration, which naturally provides geometric hints for few-shot training and HR guidance for resolution improvement. Nevertheless, there remain two major technical problems to achieving this goal. First, how to effectively exploit the geometry information from the asymmetric stereo configuration? To this end, we propose a consistency-aware training strategy, which integrates a dual-lens-consistent loss to regularize the 3D-GS optimization. Second, how to make the best use of the dual-lens training samples to effectively improve the resolution of newly synthesized views? To this end, we design a multi-reference-guided refinement module to select proper telephoto and wide-angle guided images from training samples based on the camera pose distances, and then exploit their information for high-frequency detail enhancement. Extensive experiments on simulated and real-captured datasets validate the distinct superiority of our DL-GS over various competitors on the task of HR and few-shot view synthesis.