Fig. 1.

Convolutional neural network (CNN) architecture of (a) denoising convolutional neural network (DnCNN), (b) shrinkage convolutional neural network (SCNN) and (c) deep learning-based reconstruction (dDLR). (a) DnCNN is a conventional denoising method featuring residual learning and batch normalization in hidden layers. (b) SCNN differs from DnCNN in that the activation function is a soft-shrinkage function. (c) dDLR is a plain CNN, not a residual neural network. dDLR uses discrete cosine transform (DCT) convolution to divide the data into a zero-frequency component path and a path with 48 high frequency components for denoising. A soft-shrinkage activation function is applied in both SCNN and dDLR to provide adaptive denoising at various noise levels using a single CNN without a requirement to train a unique CNN at each level. IDCT, inverse discrete cosine transform; ReLU, Rectified Linear Unit.