Block-matching and 3D filtering

All image fragments in a group are then stacked to form 3D cylinder-like shapes.

All overlapping image fragments are weight-averaged to ensure that they are filtered for noise yet retain their distinct signal.

This approach works because the noise in the chrominance channels is strongly correlated to that of the luminance channel, and it saves approximately one-third of the computing time because grouping takes up approximately half of the required computing time.

The BM3D algorithm has been extended (IDD-BM3D) to perform decoupled deblurring and denoising using the Nash equilibrium balance of the two objective functions.

[4] An approach that integrates a convolutional neural network has been proposed and shows better results (albeit with a slower runtime).