The quality of video sequences (e.g. old movies, webcam, TV broadcast) is often reduced by noise, usually
assumed white and Gaussian, being superimposed on the sequence. When denoising image sequences, rather
than a single image, the temporal dimension can be used for gaining in better denoising performance, as well
as in the algorithms' speed. This paper extends single image denoising method reported in to sequences.
This algorithm relies on sparse and redundant representations of small patches in the images. Three different
extensions are offered, and all are tested and found to lead to substantial benefits both in denoising quality and
algorithm complexity, compared to running the single image algorithm sequentially. After these modifications,
the proposed algorithm displays state-of-the-art denoising performance, while not relying on motion estimation.