  Convolutional neural network (CNN)-based image denoising methods typically
estimate the noise component contained in a noisy input image and restore a
clean image by subtracting the estimated noise from the input. However,
previous denoising methods tend to remove high-frequency information (e.g.,
textures) from the input. It caused by intermediate feature maps of CNN
contains texture information. A straightforward approach to this problem is
stacking numerous layers, which leads to a high computational cost. To achieve
high performance and computational efficiency, we propose a gated texture CNN
(GTCNN), which is designed to carefully exclude the texture information from
each intermediate feature map of the CNN by incorporating gating mechanisms.
Our GTCNN achieves state-of-the-art performance with 4.8 times fewer parameters
than previous state-of-the-art methods. Furthermore, the GTCNN allows us to
interactively control the texture strength in the output image without any
additional modules, training, or computational costs.
