Papers › FEUNet: a flexible and effective U-shaped network for image denoising
FEUNet: a flexible and effective U-shaped network for image denoising
Wencong Wu, Guannan Lv, Shicheng Liao, Yungang Zhang
Over the recent years, deep convolutional neural networks based models have been absolutely attractive in image denoising field due to their favorable performance. However, many existing deep neural network based image denoising models lack flexibility for spatially variant or real-world noise, which restricts the application of these models in real denoising scenes. In this paper, we propose a flexible and effective U-shaped network (FEUNet), which is effective in a wide range of noise levels, and can deal with spatially variant noise. The adjustable noise level map is used as the input of the FEUNet to enhance its flexibility. The U-Net is utilized to enhance the effectiveness of the proposed model. Experimental results have verified that the proposed FEUNet can obtain competitive denoising performances on many denoising tasks compared with the state-of-the-art denoising methods, which makes the proposed FEUNet well suited for the practical image denoising tasks.
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