  Recently, deep learning (DL) methods such as convolutional neural networks
(CNNs) have gained prominence in the area of image denoising. This is owing to
their proven ability to surpass state-of-the-art classical image denoising
algorithms such as BM3D. Deep denoising CNNs (DnCNNs) use many feedforward
convolution layers with added regularization methods of batch normalization and
residual learning to improve denoising performance significantly. However, this
comes at the expense of a huge number of trainable parameters. In this paper,
we address this issue by reducing the number of parameters while achieving a
comparable level of performance. We derive motivation from the improved
performance obtained by training networks using the dense-sparse-dense (DSD)
training approach. We extend this training approach to a reduced DnCNN (RDnCNN)
network resulting in a faster denoising network with significantly reduced
parameters and comparable performance to the DnCNN.
