We present an efficient algorithm for nonlocal image filtering with applications in electron cryomicroscopy. Our denoising algorithm is a rewriting of the recently proposed nonlocal mean filter. It builds on the separable property of neighborhood filtering to offer a fast parallel and vectorized implementation in contemporary shared memory computer architectures while reducing the theoretical computational complexity of the original filter. In practice, our approach is much faster than a serial, non-vectorized implementation and it scales linearly with image size. We demonstrate its efficiency in data sets fromCaulobacter crescentus tomograms and a cryoimage containing viruses and provide visual evidences attesting the remarkable quality of the nonlocal means scheme in the context of cryoimaging. With such development we provide biologists with an attractive filtering tool to facilitate their scientific discoveries.