Multifeature extracting CNN with concatenation for image denoising

Convolutional neural networks (CNNs) have made great achievements in the field of image denoising but can still be improved. We introduce a network structure, namely, multifeature extracting CNN with concatenation (McCNN), which can preserve the edge and detail information and make the denoised image easier to view. The McCNN uses different-sized convolutional kernels to extract multiple features from the input image and send them into a forward network structure after cascading these features. The forward network structure consists of five nonlinear mapping modules, which are responsible for extracting more detailed textures and other advanced features. A skip connection is integrated into the forward network structure to pass the feature maps that carry many image details, which helps to reduce image distortion. The skip connection can also reduce gradient disappearance and improve network convergence speed. The potential clean image in the contaminated image contains much more information than the noise image. The noise image is regarded as the learning objective of the network to reduce the learning burden. The experimental results demonstrate that our McCNN denoising method can effectively remove Gaussian noise in grayscale images and offers objective and subjective quality improvement compared to that of the DnCNN-S, SCNN, and DSNet models, as well as other state-of-the-art denoising methods.