Multiframe Raw-data Denoising Based on Block-matching and 3-d Filtering for Low-light Imaging and Stabilization

We consider the problem of the joint denoising of a number of rawdata images from a digital imaging sensor. In particular, we exploit a recently proposed image modeling [8] that incorporates both the signal-dependent nature of noise and the clipping of the data due to underor over-exposure of the sensor. Our denoising approach is based on the V-BM3D algorithm [5], coupled with a set of homomorphic preand post-processing transformations derived for variance-stabilization, debiasing, and declipping [6]. The spatio-temporal nonlocality of V-BM3D frees us from the need of an explicit registration of the frames. It results in a practical algorithm directly applicable to raw-data processing, in particular for heavy-noise conditions such those encountered in low-light imaging or imaging at fast shutter speeds. Experiments with synthetic images and with real raw-data from CCD sensor show the feasibility of the approach and provide an indicative measure of the advantage of multiframe versus singleframe processing.