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Attention-guided CNN for image denoising.pptx

发布时间：2022-05-31 发布人：admin 分类：说明书 资料大小：1.12M 资料格式：pptx 举报 版权申诉
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    Attention-guided CNN for image denoising 刘颜静 2020/2/24
    目 录 C O N T E N T S 01 02 03 04 Introduction Method Experiments and results Conclusion
    01 Introduction
    most of tranditional methods are faced with three major problems： LOGO manually selected parameters complex optimized algorithms certain noise task
    LOGO deep learning method: DnCNN can use a model to deal with multi denoising tasks ,but result in performance degradation DRNN:deep recursive residual network,but high computational cost and memory consumption Most of these methods neglect that complex background can hide some key features
    02 Method
    LOGO
    LOGO ADNet: SB + FEB + AB +RB SB：Sparse block uses the dilated and standard convolutions to enlarge the receptive field size for improving denoising performance FEB：Feature enhancement block integrates the global and local features of ADNet via a long path to enhance the expressive ability AB：Attention block can quickly capture the key noisy features hidden in the complex background for complex noisy tasks RB：Reconstruction block reconstruct the clean image
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