  Is it possible to recover an image from its noisy version using convolutional
neural networks? This is an interesting problem as convolutional layers are
generally used as feature detectors for tasks like classification, segmentation
and object detection. We present a new CNN architecture for blind image
denoising which synergically combines three architecture components, a
multi-scale feature extraction layer which helps in reducing the effect of
noise on feature maps, an l_p regularizer which helps in selecting only the
appropriate feature maps for the task of reconstruction, and finally a three
step training approach which leverages adversarial training to give the final
performance boost to the model. The proposed model shows competitive denoising
performance when compared to the state-of-the-art approaches.
