We present spatiotemporal denoising based on overlapped motion compensation and advanced collaborative filtering. First, noise-robust overlapped motion compensation is performed on a block basis for temporal grouping. Next, the -nearest neighbors of each block are grouped in a 3D array, and the 3D array is transformed. Then, adaptive soft thresholding is performed in the 3D transform domain. In addition, a modified weighting strategy for aggregation is applied for better visual quality. Simulation results show that the proposed algorithm improves the peak signal-to-noise ratio performance by about 2 dB in comparison with the state-of-the-art technique while providing much better subjective visual quality.