IEEE Trans Image Process - How to SAIF-ly boost denoising performance.

Tópicos

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Resumo

Spatial domain image filters (e.g., bilateral filter, non-local means, locally adaptive regression kernel) have achieved great success in denoising. Their overall performance, however, has not generally surpassed the leading transform domain-based filters (such as BM3-D). One important reason is that spatial domain filters lack efficiency to adaptively fine tune their denoising strength; something that is relatively easy to do in transform domain method with shrinkage operators. In the pixel domain, the smoothing strength is usually controlled globally by, for example, tuning a regularization parameter. In this paper, we propose spatially adaptive iterative filtering (SAIF) is the Middle Eastern/Arabic name for sword. This acronym somehow seems appropriate for what the algorithm does by precisely tuning the value of the iteration number. a new strategy to control the denoising strength locally for any spatial domain method. This approach is capable of filtering local image content iteratively using the given base filter, and the type of iteration and the iteration number are automatically optimized with respect to estimated risk (i.e., mean-squared error). In exploiting the estimated local signal-to-noise-ratio, we also present a new risk estimator that is different from the often-employed SURE method, and exceeds its performance in many cases. Experiments illustrate that our strategy can significantly relax the base algorithm's sensitivity to its tuning (smoothing) parameters, and effectively boost the performance of several existing denoising filters to generate state-of-the-art results under both simulated and practical conditions.

Resumo Limpo

spatial domain imag filter eg bilater filter nonloc mean local adapt regress kernel achiev great success denois overal perform howev general surpass lead transform domainbas filter bmd one import reason spatial domain filter lack effici adapt fine tune denois strength someth relat easi transform domain method shrinkag oper pixel domain smooth strength usual control global exampl tune regular paramet paper propos spatial adapt iter filter saif middl easternarab name sword acronym somehow seem appropri algorithm precis tune valu iter number new strategi control denois strength local spatial domain method approach capabl filter local imag content iter use given base filter type iter iter number automat optim respect estim risk ie meansquar error exploit estim local signaltonoiseratio also present new risk estim differ oftenemploy sure method exceed perform mani case experi illustr strategi can signific relax base algorithm sensit tune smooth paramet effect boost perform sever exist denois filter generat stateoftheart result simul practic condit

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