Learning deep CNNs for impulse noise removal in images

Deep learning has been widely applied in image processing and computer vision due to its powerful learning capability. Although some learning models have been proposed to suppress noise in images, most of them are developed for Gaussian noise and few are for impulse noise. This paper proposes an image recovery method based on deep convolutional neural networks for impulse noise removal. The proposed framework falls into two components: a classifier network which divides image pixels into noisy and noise-free, and a regression network which is trained for image reconstruction. In the regression network, the noise-free pixels identified by the classifier network together with the original noisy image are used for recovery of the noisy image. Furthermore, batch normalization is embedded to the network to improve denoising performance. Experimental results show that the proposed method can excellently remove impulse noise, providing clear performance improvements over other state-of-the-art denoising methods.