The recent advance in Neural Radiance Fields (NeRF), which utilizes Multilayer Perceptrons (MLP) for implicit scene representation, enables the synthesis of realistic views from new perspectives. However, degradation in the quantity and quality of input images can lead to failure in scene reconstruction and difficulty in synthesizing high-quality views. To address these limitations, this paper presents a NeRF-based framework (MBS-NeRF), which can reconstruct sharp NeRF from a limited number of motion-blurred input images for high-quality view synthesis. The framework integrates depth information as a constraint to counter the lack of sufficient view information and introduces a Motion Blur Simulation Module (MBSM) to simulate the physical formation process of motion blur. We further introduce camera trajectory optimization during the exposure process to robust the incorrect camera position. MBS-NeRF is thoroughly trained considering photometric consistency and depth supervision. Comprehensive experiments on synthetic and real datasets validate the effectiveness of the model in reconstructing sharp NeRF and achieving high-quality view synthesis from sparse, motion-blurred inputs.