Texture-guided CNN for image denoising
Multimedia Tools and Applications(2024)
摘要
Convolutional neural networks (CNNs) can effectively extract structural information in image denoising. However, they tend to ignore texture information. To tackle this problem, we present a texture-guided CNN for image denoising (TDCNN), which depends on blocks for texture extraction, refinement, and transformation to realize excellent denoising performance on both quantitative and visual metrics. A texture-extraction block combines non-local similarity and two sub-networks to extract texture and structural information. A refinement block with a stacked architecture mines accurate information from complementary features. A transformation block is used to obtain clean output images. A joint loss function, including perceptual loss and mean square error, enhances the robustness of the proposed denoiser. Experiments show that the proposed TDCNN is superior to some popular methods for denoising synthetic and real images.
更多查看译文
关键词
Non-local method,Jointed loss,CNN,Image denoising
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要