EDCNN: A Novel Network for Image Denoising
Haizhang Zou, Rushi Lan, Yanru Zhong, Zhenbing Liu, Xiaonan Luo · 2019
In recent years, deep convolutional neural network (DCNN) has achieved impressive performance in image denoising. However, the existing CNN-based methods cannot work very well on those images with high-level noise. In order to solve this problem, we propose a novel method, named enhanced deep convolution neural network (EDCNN), for image de-noising in this work. Compared with existing models, ED-CNN adopts the residual learning in both global and local manners. In particular, we further apply a residual excitation strategy that enables a short path to be built directly from the input image to output layer. The final model, composed of 52 weight layers, is much deeper than existing ones. Experimental results on standard test images have demonstrated that the proposed method outperforms several state-of-the-art de-noising algorithms in terms of both quantitative measure and visual perception quality.