A Dual-Channel Deep Convolutional Neural Network Model for Random-Valued Impulse Noise Removal

A Dual-channel Denoising Convolutional Neural Network (D-DnCNN) model for the removal of Random-Valued Impulse Noise (RVIN) is proposed. To obtain the reference image quickly, several Rank-Ordered Logarithmic absolute Difference (ROLD) statistics and one edge feature statistic are first extracted from a local window to construct a RVIN-aware feature vector which can describe the central pixel of the patch is RVIN or not. Next, a noise detector based on Deep Belief Network (DBN) is trained to map the extracted feature vectors to their corresponding noise labels to detect all noise-like pixels in the observed image. Then, under the guidance of noise labels, the Delaunay triangulation-based interpolation algorithm is exploited to restore all detected noise-like pixels quickly and generate a preliminary restored image used as reference image. Finally, the reference image and the noisy image are simultaneously fed into the D-DnCNN model to output its corresponding residual image, and the final restored image can be obtained by subtracting the residual image from the noisy image. Extensive experimental results show that, the denoising effect of the proposed D-DnCNN denoising model outperforms the existing state-of-art switching ones across a range of noise ratios, and it also works better than the ordinary single-channel DnCNN model.