A method, implemented by computer means, for processing the noise of a digital video stream comprising a series of successive images, the method comprising: - spatial filtering to denoise a current image I n of the series in order to obtain a spatially filtered image S n, n being a natural number; - determining a respective motion vector for each block of pixels of the image S n from a single image S n-1 obtained by spatial denoising filtering of the image I n-1 preceding the image I n in the series of images; - calculating a respective confidence index α n for each block B of image I n from the respective motion vector determined for the block B, where α n is representative of a correlation between the block B and a block B* of the image I n-1 corresponding to the block B; - constructing a temporal filter from the calculated confidence indices α n and applying the temporal filter to the current image I n in order to obtain a temporally filtered image T n; - spatial filtering to denoise the current image I n of the series in order to obtain a spatially filtered image D n, and - obtaining a temporally and spatially filtered image R n by applying a function combining the temporally filtered image T n and the spatially filtered image D n. the method being characterized in that the respective confidence index α n for the block B is calculated from a parameter P evaluating the correlation between a corresponding to the block B in the image D n and a block corresponding to the block B* in the image D n-1, Dn-1 being obtained by spatial denoising filtering of the image I n-1, and in that the confidence index α n is expressed as follows: ∝ n B = P B ∗ clip s + v ∗ ∝ n − 1 B *, 0,1 with clip x, min, max = { min if x < min max if x > max else x where: - s denotes an offset - v a learning speed - ∝ n-1 [B*] denotes a confidence index calculated for the block B* of image I n-1 - spatial filtering to denoise a current image I n of the series in order to obtain a spatially filtered image S n, n being a natural number; - determining a respective motion vector for each block of pixels of the image S n from a single image S n-1 obtained by spatial denoising filtering of the image I n-1 preceding the image I n in the series of images; - calculating a respective confidence index α n for each block B of image I n from the respective motion vector determined for the block B, where α n is representative of a correlation between the block B and a block B* of the image I n-1 corresponding to the block B; - constructing a temporal filter from the calculated confidence indices α n and applying the temporal filter to the current image I n in order to obtain a temporally filtered image T n; - spatial filtering to denoise the current image I n of the series in order to obtain a spatially filtered image D n, and - obtaining a temporally and spatially filtered image R n by applying a function combining the temporally filtered image T n and the spatially filtered image D n. the method being characterized in that the respective confidence index α n for the block B is calculated from a parameter P evaluating the correlation between a corresponding to the block B in the image D n and a block corresponding to the block B* in the image D n-1, Dn-1 being obtained by spatial denoising filtering of the image I n-1, and in that the confidence index α n is expressed as follows: ∝ n B = P B ∗ clip s + v ∗ ∝ n − 1 B *, 0,1 with clip x, min, max = { min if x < min max if x > max else x where: - s denotes an offset - v a learning speed - ∝ n-1 [B*] denotes a confidence index calculated for the block B* of image I n-1 the method being characterized in that the respective confidence index α n for the block B is calculated from a parameter P evaluating the correlation between a corresponding to the block B in the image D n and a block corresponding to the block B* in the image D n-1, Dn-1 being obtained by spatial denoising filtering of the image I n-1, and in that the confidence index α n is expressed as follows: ∝ n B = P B ∗ clip s + v ∗ ∝ n − 1 B *, 0,1 with clip x, min, max = { min if x < min max if x > max else x where: - s denotes an offset - v a learning speed - ∝ n-1 [B*] denotes a confidence index calculated for the block B* of image I n-1 - s denotes an offset - v a learning speed - ∝ n-1 [B*] denotes a confidence index calculated for the block B* of image I n-1