There is a Poisson inverse problem in biomedical imaging,fluorescence microscopy and so on.Since the observed measurements are damaged by a linear operator and further destroyed by Poisson noise,recovering the approxi...There is a Poisson inverse problem in biomedical imaging,fluorescence microscopy and so on.Since the observed measurements are damaged by a linear operator and further destroyed by Poisson noise,recovering the approximate original image is difficult.Motivated by the decouple scheme and the variance-stabilizing transformation(VST)strategy,we propose a method of transformed convolutional neural network(CNN)to restore the observed image.In the network,the Conv-layers play the role of a linear inverse filter and the distribution transformation simultaneously.Furthermore,there is no batch normalization(BN)layer in the residual block of the network,which is devoted to tackling with the non-Gaussian recovery procedure.The proposed method is compared with state-of-the-art Poisson deblurring algorithms,and the experimental results show the effectiveness of the method.展开更多
基金the National Natural Science Foundation of China(No.61661031)。
文摘There is a Poisson inverse problem in biomedical imaging,fluorescence microscopy and so on.Since the observed measurements are damaged by a linear operator and further destroyed by Poisson noise,recovering the approximate original image is difficult.Motivated by the decouple scheme and the variance-stabilizing transformation(VST)strategy,we propose a method of transformed convolutional neural network(CNN)to restore the observed image.In the network,the Conv-layers play the role of a linear inverse filter and the distribution transformation simultaneously.Furthermore,there is no batch normalization(BN)layer in the residual block of the network,which is devoted to tackling with the non-Gaussian recovery procedure.The proposed method is compared with state-of-the-art Poisson deblurring algorithms,and the experimental results show the effectiveness of the method.
