EDCNN: A Novel Network for Image Denoising

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.

[1]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[2]  Yuan Yan Tang,et al.  Image denoising using non-local fuzzy means , 2015, 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP).

[3]  Tong Tong,et al.  Image Super-Resolution Using Dense Skip Connections , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[4]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[5]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[6]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Ming Yang,et al.  Image Blind Denoising with Generative Adversarial Network Based Noise Modeling , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[8]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[9]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[10]  Lei Zhang,et al.  FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising , 2017, IEEE Transactions on Image Processing.

[11]  H. Sebastian Seung,et al.  Natural Image Denoising with Convolutional Networks , 2008, NIPS.

[12]  Stamatios Lefkimmiatis,et al.  Universal Denoising Networks : A Novel CNN Architecture for Image Denoising , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[13]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[14]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[15]  Stefan Harmeling,et al.  Image denoising: Can plain neural networks compete with BM3D? , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  HuiMing Li.  Deep Learning for Image Denoising , 2014 .

[17]  Yue Wu,et al.  Probabilistic Non-Local Means , 2013, IEEE Signal Processing Letters.

[18]  Feng Liu,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries in Wavelet Domain , 2009, 2009 Fifth International Conference on Image and Graphics.

[19]  Yunjin Chen,et al.  Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  David Zhang,et al.  External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising , 2017, IEEE Transactions on Image Processing.

[21]  Jitendra Malik,et al.  A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.