残余物
块(置换群论)
噪音(视频)
降噪
人工智能
计算机科学
图像(数学)
图像质量
信号(编程语言)
算法
模式识别(心理学)
数学
几何学
程序设计语言
作者
Jan-Ray Liao,Kuo-Hung Lin,Yen-Cheng Chang
标识
DOI:10.1016/j.dsp.2023.104052
摘要
Residual learning is one of the most effective components in blind image denoising. It learns to estimate the noise instead of the clean image itself. A shortcoming of residual learning is that it cannot capture hierarchical features efficiently because there are no connections between layers to extract multi-level features. Therefore, residual dense network (RDN) adds dense connections among layers in the residual block to facilitate extraction of hierarchical features. Although RDN is a superior non-blind denoiser, it is only better than state-of-the-art methods in blind denoising when noise level is high. In this paper, we employ the concept of Wiener filters and add a non-residual network as a guidance for RDN. The non-residual network predicts the signal instead of the noise to assist RDN in achieving satisfactory performance across all noise levels. Then, a guidance network combines the outputs from both residual and non-residual networks to generate the final denoised output. Experimental results show that the new architecture performs better than state-of-the-art blind denoisers quantitatively and image quality of the new method is also better than existing methods.
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