Journal of Applied Optics, Volume. 41, Issue 2, 288(2020)
Image denoising algorithm based on wavelet transform and convolutional neural network
[3] A BIJALWAN, A GOYAL, N SETHI. Wavelet transform based image denoise using threshold approaches. International Journal of Engineering & Advanced Technology, 218-221(2012).
[5] [5] STARCK J L, CES E J, DONOHO D L. The curvelet transfm f image denoising[J]. IEEE Transactions on Image Processing, 2002, 11(6): 670684.
[6] [6] YI Q, WENG Y, HE J. Image denoise based on curvelet transfm[C]. USA: IEEE Wkshop on Electronics, Computer & Applications, 2014: 14416899.
[8] 王敏, Min WANG, 周磊, Lei ZHOU, 周树道, Shudao ZHOU. Image SVD denoising based on PSNR and wavelet directional feature. Journal of Applied Optics, 34, 85-89(2013).
[9] 吴海兵, Haibing WU, 张良, Liang ZHANG, 顾国华, Guohua GU. Color image enhancement based on LLL tricolor image denoising and fusion. Journal of Applied Optics, 39, 57-63(2018).
[11] [11] JAIN V, SEUNG H S. Natural image denoising with convolutional wks[C]International Conference on Neural Infmation Processing Systems. NY: Curran Associates Inc., 2008: 769776.
[12] [12] HARMELING S, SCHULER C J, BURGER H C. Image denoising: Can plain neural wks compete with BM3D[C]IEEE Conference on Computer Vision Pattern Recognition. USA: IEEE Computer Society, 2012: 23922399.
[13] K ZHANG, W ZUO, Y CHEN. Beyond a Gaussian denoiser: Residual learningof deep CNN for image denoising. IEEE Transactions on Image Processing, 26, 3142-3155(2016).
[14] K ZHANG, W ZUO, L ZHANG. FFDNet: Toward a fast and flexible solution for CNN based image denoising. IEEE Transactions on Image Processing, 27, 4608-4622(2017).
[15] 吴从中, Congzhong WU, 陈曦, Xi CHEN, 季栋, Dong JI. Image denoising via residual network based on perceptual loss. Journal of Image and Graphics, 23, 1483-1491(2018).
[16] 吕永标, Yongbiao LYU, 赵建伟, Jianwei ZHAO, 曹飞龙, Feilong CAO. Image denoising algorithm based on composite convolution neural network. Pattern Recognition and Artificial Intelligence, 30, 97-105(2017).
[17] 马红强, Hongqiang MA, 马时平, Shiping MA, 许悦雷, Yuelei XU. Adaptive image denoising based on improved stacked sparse denoising auto-encoder. Acta Optica Sinica, 38, 128-135(2018).
[18] D ZORAN, Y WEISS. From learning models of natural image patches to whole image restoration. IEEE, 2011, 479-486(6669).
[20] [20] GU S, ZHANG L, ZUO W, et al. Weighted nuclear nm minimization with application to image denoising[C] Computer Vision & Pattern Recognition. USA: IEEE, 2014: 28622869.
[21] [21] HE K, ZHANG X, REN S, et al. Deep residual learning f image recognition[C]IEEE Conference on Computer Vision Pattern Recognition. USA: IEEE, 2016: 770778.
[22] [22] SCHT U, ROTH S. Shrinkage fields f effective image restation[C]IEEE Conference on Computer Vision Pattern Recognition. USA: IEEE, 2014: 27742781.
[23] C DONG, C C LOY, K HE. Image super-resolution using deep convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2014).
[24] [24] DO M N, VETTERLI M. Contourlets: A new directional multiresolution image representation[C]Conference Recd of the ThirtySixth Asilomar Conference on Signals, Systems Computers, 2002. USA: IEEE, 2002: 497501.
Get Citation
Copy Citation Text
Qingjiang CHEN, Xiaohan SHI, Yuzhou CHAI. Image denoising algorithm based on wavelet transform and convolutional neural network[J]. Journal of Applied Optics, 2020, 41(2): 288
Category:
Received: May. 7, 2019
Accepted: --
Published Online: Apr. 23, 2020
The Author Email: SHI Xiaohan (910762960@qq.com)