Journal of Applied Optics, Volume. 41, Issue 2, 288(2020)

Image denoising algorithm based on wavelet transform and convolutional neural network

Qingjiang CHEN1, Xiaohan SHI1、*, and Yuzhou CHAI2
Author Affiliations
  • 1College of Science, Xi’an University of Architecture and Technology, Xi’an 710055, China
  • 2Xi’an Institute of Space Radio Technology, Xi’an 710000, China
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    References(24)

    [3] BIJALWAN A, GOYAL A, SETHI N. Wavelet transform based image denoise using threshold approaches[J]. 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, WANG Min, 周磊, ZHOU Lei, 周树道, ZHOU Shudao. Image SVD denoising based on PSNR and wavelet directional feature[J]. Journal of Applied Optics, 34, 85-89(2013).

    [9] 吴海兵, WU Haibing, 张良, ZHANG Liang, 顾国华, GU Guohua. Color image enhancement based on LLL tricolor image denoising and fusion[J]. 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] ZHANG K, ZUO W, CHEN Y. Beyond a Gaussian denoiser: Residual learningof deep CNN for image denoising[J]. IEEE Transactions on Image Processing, 26, 3142-3155(2016).

    [14] ZHANG K, ZUO W, ZHANG L. FFDNet: Toward a fast and flexible solution for CNN based image denoising[J]. IEEE Transactions on Image Processing, 27, 4608-4622(2017).

    [15] 吴从中, WU Congzhong, 陈曦, CHEN Xi, 季栋, JI Dong. Image denoising via residual network based on perceptual loss[J]. Journal of Image and Graphics, 23, 1483-1491(2018).

    [16] 吕永标, LYU Yongbiao, 赵建伟, ZHAO Jianwei, 曹飞龙, CAO Feilong. Image denoising algorithm based on composite convolution neural network[J]. Pattern Recognition and Artificial Intelligence, 30, 97-105(2017).

    [17] 马红强, MA Hongqiang, 马时平, MA Shiping, 许悦雷, XU Yuelei. Adaptive image denoising based on improved stacked sparse denoising auto-encoder[J]. Acta Optica Sinica, 38, 128-135(2018).

    [18] ZORAN D, WEISS Y. From learning models of natural image patches to whole image restoration[J]. 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] DONG C, LOY C C, HE K. Image super-resolution using deep convolutional networks[J]. 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.

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    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

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    Paper Information

    Category:

    Received: May. 7, 2019

    Accepted: --

    Published Online: Apr. 23, 2020

    The Author Email: Xiaohan SHI (910762960@qq.com)

    DOI:10.5768/JAO202041.0202001

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