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on the coefficients of the shape-adaptive DCT. The threshold includes the noise variance and
we show how a real camera noise characteristic can be integrated. To evaluate our method
we compare it with two state-of-the-art algorithms: a PCA-based CFA denoising and a BM3D-
based denoising that uses noise variance estimation. While our method achieves competitive
results in terms of PSNR, we show that our method can lead to beter visual quality with lower
computational cost. An additional temporal denoising step is proposed, which effectively re-
duces temporal flickering in real camera video sequences.
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