论文标题
封闭式纹理CNN，可高效且可配置的图像Denoising
Gated Texture CNN for Efficient and Configurable Image Denoising
论文作者
论文摘要
基于卷积的神经网络（CNN）基于图像denoising方法通常估计嘈杂输入图像中包含的噪声分量，并通过从输入中减去估计的噪声来恢复干净的图像。但是，以前的降级方法倾向于从输入中删除高频信息（例如纹理）。它由CNN的中间特征图引起，其中包含纹理信息。解决此问题的一种直接方法是堆叠众多层，这导致了高计算成本。为了达到高性能和计算效率，我们提出了一个封闭式纹理CNN（GTCNN），该纹理旨在通过合并门控机制来仔细地将CNN的每个中间特征图中的纹理信息仔细排除。我们的GTCNN的参数比以前的最新方法少4.8倍。此外，GTCNN使我们能够在没有任何其他模块，培训或计算成本的情况下交互式地控制输出图像中的纹理强度。
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.
