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CNN模型Retina Face parameters

mulfreq_restorephase_rel.m

We consider the problem of the joint denoising of a number of raw- data 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 under- or over-exposure of the sensor. Our denoising approach is based on the V-BM3D algorithm [5], coupled with a set of homomorphic pre- and post-processing trans- formations derived for variance-stabilization, debiasing, and declip- ping [6]. The spatio-temporal nonlocality of V-BM3D frees us from the need of an explicit registration of the frames. It results in a prac- tical algorithm directly applicable to raw-data processing, in partic- ular for heavy-noise conditions such those encountered in low-light imaging or imaging at fast shutter speeds.