Skip to main content
Springer Nature Link
Log in

Texture-guided CNN for image denoising

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Convolutional neural networks (CNNs) can effectively extract structural information in image denoising. However, they tend to ignore texture information. To tackle this problem, we present a texture-guided CNN for image denoising (TDCNN), which depends on blocks for texture extraction, refinement, and transformation to realize excellent denoising performance on both quantitative and visual metrics. A texture-extraction block combines non-local similarity and two sub-networks to extract texture and structural information. A refinement block with a stacked architecture mines accurate information from complementary features. A transformation block is used to obtain clean output images. A joint loss function, including perceptual loss and mean square error, enhances the robustness of the proposed denoiser. Experiments show that the proposed TDCNN is superior to some popular methods for denoising synthetic and real images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

References

  1. Healey GE, Kondepudy R (1994) Radiometric CCD camera calibration and noise estimation[J]. IEEE Trans Pattern Anal Mach Intell 16(3):267–276

    Article  Google Scholar 

  2. Li Z, Wu J (2019) Learning deep cnn denoiser priors for depth image inpainting. Appl Sci 9(6):1103

    Article  Google Scholar 

  3. Yu X, Fu Z, Ge C (2021) A multi-scale generative adversarial network for real-world image denoising. Signal, Image Vid Process 16:1–8. https://doi.org/10.1007/s11760-021-01984-5

    Article  Google Scholar 

  4. Pitas I, Venetsanopoulos A (1986) Nonlinear mean filters in image processing. IEEE Trans Acoust Speech Signal Process 34(3):573–584. https://doi.org/10.1109/TASSP.1986.1164857

    Article  Google Scholar 

  5. Huang T (1972) Stability of two-dimensional recursive filters. IEEE Trans Audio Electroacoust 20(2):158–163

    Article  MathSciNet  Google Scholar 

  6. Hong SW, Bao P (2000) An edge-preserving subband coding model based on non-adaptive and adaptive regularization. Image Vis Comput 18(8):573–582

    Article  Google Scholar 

  7. Dabov K, Foi A, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans Image Process 16(8):2080–2095

    Article  MathSciNet  Google Scholar 

  8. Gu S, Zhang L, Zuo W, et al (2014) Weighted nuclear norm minimization with application to image denoising, in Proceedings of the IEEE conference on computer vision and pattern recognition: 2862–2869

  9. Malfait M, Roose D (1997) Wavelet-based image denoising using a Markov random field a priori model. IEEE Trans Image Process 6(4):549–565

    Article  Google Scholar 

  10. Jo Y, Chun SY, Choi J (2021) Rethinking deep image prior for denoising, Proceedings of the IEEE/CVF International Conference on Computer Vision: 5087–5096

  11. Tian C, Fei L, Zheng W et al (2020) Deep learning on image denoising: An overview. Neural Netw 131:251–275

    Article  Google Scholar 

  12. Liu D, Wen B, Liu X, Wang Z, Huang TS (2017) When image denoising meets high-level vision tasks: A deep learning approach, arXiv preprintarXiv:1706.04284

  13. Zhang K, Zuo W, Chen Y, Meng D, Zhang L (2017) Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising. IEEE Trans Image Process 26(7):3142–3155

    Article  MathSciNet  Google Scholar 

  14. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, in IEEE Conf Comput Vis Pattern Recognit, pp. 770–778

  15. Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift, in International Conference on Machine Learning, pp. 448–456

  16. Tai Y, Yang J, Liu X, Xu C (2017) Memnet: A persistent memory network for image restoration, in Proceedings of the IEEE international conference on computer vision: 4539–4547

  17. Chen C, Xiong Z, Tian X, et al. (2018) Deep boosting for image denoising, in Proceedings of the European Conference on Computer Vision: 3–18. https://doi.org/10.1007/978-3-030-01252-6_1

  18. Bae W, Yoo J, Ye JC (2017) Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification, in IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1141–1149

  19. Tian C, Xu Y, Li Z, Zuo W, Fei L, Liu H (2020) Attention-guided CNN for Image Denoising. Neural Netw 124:117–129

    Article  Google Scholar 

  20. Zhang K, Zuo W, Gu S, Zhang L (2017) Learning deep CNN denoiser prior for image restoration, in Proceedings of the IEEE conference on computer vision and pattern recognition: 3929–3938

  21. Guo S, Yan Z, Zhang K, et al. (2019) Toward convolutional blind denoising of real photographs in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition: 1712–1722

  22. Chen J, Chen J, Chao H, et al. (2018) Image blind denoising with generative adversarial network based noise modeling[C]//Proceedings of the IEEE conference on computer vision and pattern recognition: 3155–3164

  23. Ren X, Li J, Hua Z et al (2021) Consistent image processing based on co-saliency[J]. CAAI Trans Intel Technol 6(3):324–337

    Article  Google Scholar 

  24. Jifara W, Jiang F, Rho S et al (2019) Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput 75(2):704–718

    Article  Google Scholar 

  25. Wang T, Sun M, Hu K (2017) Dilated deep residual network for image denoising, in IEEE 29th international conference on tools with artificial intelligence: 1272–1279

  26. Zhang K, Zuo W, Zhang L (2018) FFDNet: Toward a fast and flexible solution for CNN-based image denoising. IEEE Trans Image Process 27(9):4608–4622

    Article  MathSciNet  Google Scholar 

  27. Liu P, Zhang H, Wang J, et al (2022) Robust deep ensemble method for real-world image denoising. Neurocomputing 512:1–14

  28. Liu D, Wen B, Fan Y, et al. (2018) Non-local recurrent network for image restoration, Advances in neural information processing systems, 31

  29. Zhang X, Sun Y, Liu H et al (2021) Improved clustering algorithms for image segmentation based on non-local information and back projection. Inf Sci 550:129–144

    Article  MathSciNet  Google Scholar 

  30. Li C, Qu X, Gnanasambandam A, et al. (2021) Photon-limited object detection using non-local feature matching and knowledge distillation, in Proceedings of the IEEE/CVF International Conference on Computer Vision: 3976–3987

  31. Huang G, Bors AG (2021) Region-based non-local operation for video classification, in International Conference on Pattern Recognition: 10010–10017

  32. Mei Y, Fan Y, Zhou Y (2021) Image super-resolution with non-local sparse attention, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: 3517–3526

  33. Rousseau F (2010) Alzheimer’s Disease Neuroimaging Initiative, “A non-local approach for image super-resolution using intermodality priors.” Med Image Anal 14(4):594–605

    Article  Google Scholar 

  34. Zhang Y, Li K, Li K, et al. (2019) Residual non-local attention networks for image restoration, arXiv preprintarXiv:1903.10082

  35. Park Y, Jeon M, Lee J et al (2022) MCW-Net: Single image deraining with multi-level connections and wide regional non-local blocks. Signal Processing: Image Communication 105:116701

    Google Scholar 

  36. Shi C, Pun CM (2019) Adaptive multi-scale deep neural networks with perceptual loss for panchromatic and multispectral images classification. Inf Sci 490:1–17

    Article  MathSciNet  Google Scholar 

  37. Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution, in European conference on computer vision. Springer, Cham: 694–711. https://doi.org/10.1007/978-3-319-46475-6_43

  38. Lucas A, Lopez-Tapia S, Molina R et al (2019) Generative adversarial networks and perceptual losses for video super-resolution. IEEE Trans Image Process 28(7):3312–3327

    Article  MathSciNet  Google Scholar 

  39. Zhang X, Ng R, Chen Q (2018) Single image reflection separation with perceptual losses, in Proceedings of the IEEE conference on computer vision and pattern recognition: 4786–4794

  40. Jiang L, Shi S, Qi X, et al. (2018) Gal: Geometric adversarial loss for single-view 3d-object reconstruction, in Proceedings of the European conference on computer vision: 802–816. https://doi.org/10.1007/978-3-030-01237-3_49

  41. Rad MS, Bozorgtabar B, Marti UV, et al. (2019) Srobb: Targeted perceptual loss for single image super-resolution, in Proceedings of the IEEE/CVF International Conference on Computer Vision: 2710–2719

  42. Wang X, Yu K, Wu S, et al. (2018) Esrgan: Enhanced super-resolution generative adversarial networks, in Proceedings of the European conference on computer vision, workshops: 0–0

  43. Agarap AF (2018) Deep learning using rectified linear units (relu), arXiv preprintarXiv:1803.08375

  44. Park B, Yu S, Jeong J (2019) Densely connected hierarchical network for image denoising, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: 0–0

  45. Wen Z, Guan J, Zeng T et al (2020) Residual network with detail perception loss for single image super-resolution. Comput Vis Image Understand 199:103007. https://doi.org/10.1016/j.cviu.2020.103007

    Article  Google Scholar 

  46. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv preprintarXiv:1409.1556

  47. Deng J, Dong W, Socher R, et al. (2009) Imagenet: A large-scale hierarchical image database, inIEEE conference on computer vision and pattern recognition: 248–255

  48. Wang X, Girshick R, Gupta A, et al. (2018) Non-local neural networks, in Proceedings of the IEEE conference on computer vision and pattern recognition: 7794–7803

  49. Martin D, Fowlkes C, Tal D, Malik J (2001) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics, in Proceedings Eighth IEEE International Conference on Computer Vision 2:416–423

  50. Xu J, Li H, Liang Z, Zhang D, Zhang L (2018) Real-world noisy image denoising: A new benchmark, arXiv preprint arXiv:1804.02603

  51. Roth S, Black MJ (2005) Fields of experts: A framework for learning image priors. IEEE Comput Soc Conf Comput Vis Pattern Recognit 2:860–867. https://doi.org/10.1109/CVPR.2005.160

    Article  Google Scholar 

  52. Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2009) Non-local sparse models for image restoration, in Proc Int Conf Comput Vis, pp. 2272–2279

  53. Franzen R (1999) Kodak lossless true color image suite, source: http://r0k.us/graphics/kodak4. Retrieved October 19, 2021

  54. Nam S, Hwang Y, Matsushita Y, Joo Kim S (2016) A holistic approach to cross-channel image noise modeling and its application to image denoising, in Proc IEEE Conf Comput Vis Pattern Recognit, pp. 1683–1691

  55. Paszke A, Gross S, Chintala S, Chanan G (2017) Pytorch. Comput Software Vers 1(10):2

    Google Scholar 

  56. Zhang L, Vaddadi S, Jin H, et al. (2009) Multiple view image denoising[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE: 1542–1549

  57. Xu M, Xie X (2022) NFCNN: toward a noise fusion convolutional neural network for image denoising[J]. SIViP 16(1):175–183

    Article  Google Scholar 

  58. Schmidt U, Roth S (2014) Shrinkage fields for effective image restoration, in Proc IEEE Conf Comput Vis Pattern Recognit, pp. 2774–2781

  59. Tian C, Xu Y, Fei L et al (2019) Enhanced CNN for image denoising. CAAI Trans Intell Technol 4(1):17–23

    Article  Google Scholar 

  60. Zoran D, Weiss Y (2011) From learning models of natural image patches to whole image restoration, in IEEE International Conference on Computer Vision, pp. 479–486

  61. Chen Y, Pock T (2016) Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration. IEEE Trans Pattern Anal Mach Intell 99:1–1

    Google Scholar 

  62. Burger HC, Schuler CJ, Harmeling S (2012) Image denoising: Can plain neural networks compete with BM3D? in IEEE Conference on Computer Vision and Pattern Recognition, pp. 2392–2399

  63. Yang X, Xu Y, Quan Y, Ji H (2020) Image Denoising via Sequential Ensemble Learning. IEEE Trans Image Process 29:5038–5049. https://doi.org/10.1109/TIP.2020.2978645

    Article  Google Scholar 

  64. Lee B, Ku B, Kim W, Ko H (2021) Two-Stream Learning-Based Compressive Sensing Network With High-Frequency Compensation for Effective Image Denoising. IEEE Access 9:91974–91982. https://doi.org/10.1109/ACCESS.2021.3091971

    Article  Google Scholar 

  65. Ofir N, Keller Y (2021) Multi-scale Processing of Noisy Images using Edge Preservation Losses, in International Conference on Pattern Recognition, pp. 1–8. https://doi.org/10.1109/ICPR48806.2021.9413325

  66. Wang Y, Chang D, Zhao Y (2021) A new blind image denoising method based on asymmetric generative adversarial network. IET Image Proc 15(6):1260–1272

    Article  Google Scholar 

  67. Scetbon M, Elad M, Milanfar P (2021) Deep k-svd denoising. IEEE Trans Image Process 30:5944–5955

    Article  MathSciNet  Google Scholar 

  68. Dey S, Bhattacharya R, Schwenker F et al (2021) Median Filter Aided CNN Based Image Denoising: An Ensemble Aprroach. Algorithms 14(4):109

    Article  Google Scholar 

  69. Tang Y, Sun J, Jiang A et al (2021) Adaptive graph filtering with intra-patch pixel smoothing for image denoising. Circ Syst Signal Process 40(11):5381–5400

    Article  Google Scholar 

  70. Liang J, Chen P, Wu M (2021) Research on an Image Denoising Algorithm based on Deep Network Learning. J Phys Conf Ser IOP Publish 1802(3):032112

    Article  Google Scholar 

  71. Wu X, Liu M, Cao Y, et al. (2020) Unpaired learning of deep image denoising, in European conference on computer vision. Springer, Cham: 352–368. https://doi.org/10.1007/978-3-030-58548-8_21

  72. Zheng M, Zhi K, Zeng J, et al. (2022) A Hybrid CNN for Image Denoising, J Art Intell Technol https://doi.org/10.37965/jait.2022.0101

  73. Zhang Q, Xiao J, Tian C, et al. (2022) A robust deformed convolutional neural network (CNN) for image denoising, CAAI Transactions on Intelligence Technology. https://doi.org/10.1049/cit2.12110

  74. Yu S, Park B, Jeong J (2019) Deep iterative down-up cnn for image denoising, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops: 0–0

  75. Zamir SW, Arora A, Khan S, et al. (2021) Multi-stage progressive image restoration in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: 14821–14831

  76. Gurrola-Ramos J, Dalmau O, Alarcón TE (2021) A Residual Dense U-Net Neural Network for Image Denoising. IEEE Access 9:31742–31754. https://doi.org/10.1109/ACCESS.2021.3061062

    Article  Google Scholar 

  77. Fan CM, Liu TJ, Liu KH, et al. (2022) Selective Residual M-Net for Real Image Denoising[C]//2022 30th European Signal Processing Conference (EUSIPCO). IEEE: 469–473

  78. Fang F, Li J, Yuan Y et al (2020) Multilevel edge features guided network for image denoising[J]. IEEE Trans Neural Netw Learn Syst 32(9):3956–3970

    Article  Google Scholar 

  79. Park B, Yu S, Jeong J (2019) Densely Connected Hierarchical Network for Image Denoising. IEEE/CVF Conf Comput Vis Pattern Recognit Workshops 2019:2104–2113

    Google Scholar 

  80. Makitalo M, Foi A (2012) Optimal inversion of the generalized anscombe transformation for Poisson-gaussian noise. IEEE Trans Image Process 22(1):91–103

    Article  MathSciNet  Google Scholar 

  81. Luo E, Chan SH, Nguyen TQ (2015) Adaptive image denoising by targeted databases. IEEE Trans Image Process 24(7):2167–2181

    Article  MathSciNet  Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 62201468, in part by the Shandong Provincial Natural Science Foundation under Grant ZR2023QGO74, in part by the China Postdoctoral Science Foundation under Grant 2022TQ0259 and 2022M722599, in part by the  Jiangsu Association for Science and Technology under Grant JSTJ-2023-017, in part by the Suzhou Gusu Leading Talent Project of Science and Technology Innovation and Entrepreneurship under Grant ZXL2023170

Author information

Authors and Affiliations

  1. School of Economics and Management, Harbin Institute of Technology at Weihai, Weihai, 264209, China

    Qi Zhang

  2. School of Computer Science, Central South University, Changsha, 410083, China

    Jingyu Xiao & Shichao Zhang

  3. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chunwei Lin

  4. School of Software, Northwestern Polytechnical University, Xi’an, 710129, Shaanxi, China

    Chunwei Tian

  5. Yangtze River Delta Research Institute, Northwestern Polytechnical University, Taicang, 215400, China

    Chunwei Tian

  6. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China

    Chengyuan Zhang

Authors
  1. Qi Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jingyu Xiao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Shichao Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jerry Chunwei Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Chunwei Tian
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Chengyuan Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Qi Zhang or Shichao Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Q., Xiao, J., Zhang, S. et al. Texture-guided CNN for image denoising. Multimed Tools Appl 83, 63949–63973 (2024). https://doi.org/10.1007/s11042-023-17670-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17670-2

Keywords