此类方法能够处理不同的模糊源。 Figure 3: DeblurGAN generator architecture. DeblurGAN contains two strided convolution blocks with stride 1/2, nine residual blocks [13] and two transposed convolution blocks. Each ResBlock consists of a convolution layer, instance normalization layer, and ReLU activatio...
a.取消所有pooling层。G网络中使用转置卷积(transposed convolutional layer)进行上采样,D网络中用加入stride的卷积代替pooling。 b.在D和G中均使用batch normalization c.去掉FC层,使网络变为全卷积网络 d.G网络中使用ReLU作为激活函数,最后一层使用tanh
此类方法能够处理不同的模糊源。 Figure 3: DeblurGAN generator architecture. DeblurGAN contains two strided convolution blocks with stride 1/2, nine residual blocks [13] and two transposed convolution blocks. Each ResBlock consists of a convolution layer, instance normalization layer, and ReLU activatio...
此类方法能够处理不同的模糊源。 Figure 3: DeblurGAN generator architecture. DeblurGAN contains two strided convolution blocks with stride 1/2, nine residual blocks [13] and two transposed convolution blocks. Each ResBlock consists of a convolution layer, instance normalization layer, and ReLU activatio...