Spike camera is a kind of neuromorphic camera that records dynamic scenes by firing a stream of binary spikes with extremely high temporal resolution. It demonstrates great potential for vision tasks in high-speed scenarios. One limitation in its current implementation is the relatively low spatial resolution. This paper develops a network called Spk2SRImgNet to super-resolve high resolution images from low resolution spike stream. However, fluctuations in spike stream hinder the performance of spike camera super resolution. To address this issue, we propose a motion aligned collaborative filtering (MACF) module, which is motivated by key ideas in classic image restoration schemes to mitigate fluctuations in spike data. MACF leverages the temporal similarity of spike stream to acquire similar features from neighboring moments via motion alignment. To separate disturbances from features, MACF filters these similar features jointly in transform domain to exploit representation sparsity, and generates refinement features that will be used to update initial fluctuated features. Specifically, MACF designs an inverse motion alignment operation to map these refinement features back to their original positions. The initial features are aggregated with the repositioned refinement features to enhance reliability. Experimental results demonstrate that the proposed method achieves state-of-the-art performance compared with existing methods. The code will be made publicly available.