Image Denoising via CNNs: An Adversarial Approach
This addresses image quality improvement for applications like photography or medical imaging, but it is incremental as it builds on existing CNN and adversarial training techniques.
The paper tackles blind image denoising by proposing a new CNN architecture that combines multi-scale feature extraction, l_p regularization, and adversarial training, achieving competitive performance compared to state-of-the-art methods.
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.