IEEE Trans Image Process - Spatial sparsity-induced prediction (SIP) for images and video: a simple way to reject structured interference.

Tópicos

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Resumo

We propose a prediction technique that is geared toward forming successful estimates of a signal based on a correlated anchor signal that is contaminated with complex interference. The corruption in the anchor signal involves intensity modulations, linear distortions, structured interference, clutter, and noise just to name a few. The proposed setup reflects nontrivial prediction scenarios involving images and video frames where statistically related data is rendered ineffective for traditional methods due to cross-fades, blends, clutter, brightness variations, focus changes, and other complex transitions. Rather than trying to solve a difficult estimation problem involving nonstationary signal statistics, we obtain simple predictors in linear transform domain where the underlying signals are assumed to be sparse. We show that these simple predictors achieve surprisingly good performance and seamlessly allow successful predictions even under complicated cases. None of the interference parameters are estimated as our algorithm provides completely blind and automated operation. We provide a general formulation that allows for nonlinearities in the prediction loop and we consider prediction optimal decompositions. Beyond an extensive set of results on prediction and registration, the proposed method is also implemented to operate inside a state-of-the-art compression codec and results show significant improvements on scenes that are difficult to encode using traditional prediction techniques.

Resumo Limpo

propos predict techniqu gear toward form success estim signal base correl anchor signal contamin complex interfer corrupt anchor signal involv intens modul linear distort structur interfer clutter nois just name propos setup reflect nontrivi predict scenario involv imag video frame statist relat data render ineffect tradit method due crossfad blend clutter bright variat focus chang complex transit rather tri solv difficult estim problem involv nonstationari signal statist obtain simpl predictor linear transform domain under signal assum spars show simpl predictor achiev surpris good perform seamless allow success predict even complic case none interfer paramet estim algorithm provid complet blind autom oper provid general formul allow nonlinear predict loop consid predict optim decomposit beyond extens set result predict registr propos method also implement oper insid stateoftheart compress codec result show signific improv scene difficult encod use tradit predict techniqu

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