Dynamic Residual Dense Network for Image Denoising

Sensors

Yunfang Zhu¹  Xin Du²  Yuda Song² 

[1] Computer Science & Information Engineering, Zhejiang Gongshang University, Hangzhou 310027, China;Information Science & Electronic Engineering, Zhejiang University, Hangzhou 310027, China;

关键词: noise reduction;    image restoration;    deep learning;    dynamic network;   

DOI  :  10.3390/s19173809

来源: DOAJ

【 摘 要 】

Deep convolutional neural networks have achieved great performance on various image restoration tasks. Specifically, the residual dense network (RDN) has achieved great results on image noise reduction by cascading multiple residual dense blocks (RDBs) to make full use of the hierarchical feature. However, the RDN only performs well in denoising on a single noise level, and the computational cost of the RDN increases significantly with the increase in the number of RDBs, and this only slightly improves the effect of denoising. To overcome this, we propose the dynamic residual dense network (DRDN), a dynamic network that can selectively skip some RDBs based on the noise amount of the input image. Moreover, the DRDN allows modifying the denoising strength to manually get the best outputs, which can make the network more effective for real-world denoising. Our proposed DRDN can perform better than the RDN and reduces the computational cost by $40-50\%$ . Furthermore, we surpass the state-of-the-art CBDNet by 1.34 dB on the real-world noise benchmark.

【 授权许可】

Unknown