Skip to main content
Springer Nature Link
Log in

DRB-Net: Dilated Residual Block Network for Infrared Image Restoration

  • Conference paper
  • First Online:
Advances in Visual Computing (ISVC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13599))

Included in the following conference series:

  • 1037 Accesses

Abstract

Infrared (IR) spectroscopic imaging offers label-free visualization of sample heterogeneity via spatially localized chemical information. This spatial-spectral data set is amenable to computational algorithms that highlight functional properties of the sample. Although Fourier transform IR (FT-IR) imaging provides reliable analytical information over a wide spectral profile, long data acquisition times are a major challenge impeding broad adoptability. Discrete frequency (DF) IR imaging is considerably faster, first by reducing the total number of spectral frequencies acquired to only those necessary for the task, and second by using substantially higher optical power via IR lasers. Further acceleration of imaging is hindered by high laser noise and usually relies on time-consuming averaging of ensemble measurements to achieve useful signal-to-noise ratio (SNR). Here, we develop a novel convolutional neural network (CNN) architecture capable of denoising discrete frequency infrared (DFIR) images in real-time, removing the need for excessive co-averaging, thereby reducing the total data acquisition time accordingly. Our architecture is based on dilated residual block network (DRB-Net), which outperforms state-of-the-art CNN models for image denoising task. To validate the robustness of DRB-Net, we demonstrate its efficacy on various unseen samples including SU-8 targets, polymers, cells, and prostate tissues. Our findings demonstrate that DRB-Net recovers high-quality data from noisy input without supervision and with minimal computation time.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Krafft, C., Steiner, G., Beleites, C., Salzer, R.: Disease recognition by infrared and Raman spectroscopy. J. Biophoton. 2, 13–28 (2009). https://doi.org/10.1002/jbio.200810024

    Article  Google Scholar 

  2. Tiwari, S., Falahkheirkhah, K., Cheng, G., Bhargava, R.: Colon cancer grading using infrared spectroscopic imaging-based deep learning. Appl. Spectrosc. (2022). https://doi.org/10.1177/00037028221076170

    Article  Google Scholar 

  3. Nallala, J., et al.: Infrared imaging as a cancer diagnostic tool: introducing a new concept of spectral barcodes for identifying molecular changes in colon tumors. Cytometry A 83, 294–300 (2013). https://doi.org/10.1002/cyto.a.22249

    Article  Google Scholar 

  4. Wood, B.R., Chiriboga, L., Yee, H., Quinn, M.A., McNaughton, D., Diem, M.: Fourier transform infrared (FTIR) spectral mapping of the cervical transformation zone, and dysplastic squamous epithelium. Gynecol. Oncol. 93, 59–68 (2004)

    Article  Google Scholar 

  5. Zimmermann, E., et al.: Detection and quantification of myocardial fibrosis using stain-free infrared spectroscopic imaging. Arch. Pathol. Lab. Med. (2021). https://doi.org/10.5858/arpa.2020-0635-OA

    Article  Google Scholar 

  6. Schnell, M., et al.: All-digital histopathology by infrared-optical hybrid microscopy. Proc. Natl. Acad. Sci. U.S.A. 117, 201912400 (2020). https://doi.org/10.1073/pnas.1912400117

    Article  Google Scholar 

  7. Chen, G., Qian, S.E.: Denoising and dimensionality reduction of hyperspectral imagery using wavelet packets, neighbour shrinking and principal component analysis. Remote Sens. Lett. 30, 4889–4895 (2009). https://doi.org/10.1080/01431160802653724

    Article  Google Scholar 

  8. Berisha, S., et al.: SIproc: an open-source biomedical data processing platform for large hyperspectral images. Analyst 142, 1350–1357 (2017). https://doi.org/10.1039/C6AN02082H

    Article  Google Scholar 

  9. Zimmermann, B., Kohler, A.: Optimizing Savitzky-Golay parameters for improving spectral resolution and quantification in infrared spectroscopy. Appl. Spectrosc. 67, 892–902 (2013). https://doi.org/10.1366/12-06723

    Article  Google Scholar 

  10. Zhang, K., Zuo, W., Gu, S., Zhang, L.: Learning deep CNN denoiser prior for image restoration. In: Proc. 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, pp. 2808–2817 (2017). https://doi.org/10.48550/arxiv.1704.03264

  11. Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Proc. 2005 IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognition, CVPR 2005. II, pp. 60–65 (2005). https://doi.org/10.1109/CVPR.2005.38

  12. Zhang, K., Zuo, W., Zhang, L.: FFDNet: toward a fast and flexible solution for CNN based image denoising. IEEE Trans. Image Process. 27, 4608–4622 (2017). https://doi.org/10.1109/TIP.2018.2839891

    Article  MathSciNet  Google Scholar 

  13. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007). https://doi.org/10.1109/TIP.2007.901238

    Article  MathSciNet  Google Scholar 

  14. Rabie, T.: Robust estimation approach for blind denoising. IEEE Trans. Image Process. 14, 1755–1765 (2005). https://doi.org/10.1109/TIP.2005.857276

    Article  Google Scholar 

  15. Liu, C., Szeliski, R., Kang, S.B., Zitnick, C.L., Freeman, W.T.: Automatic estimation and removal of noise from a single image. IEEE Trans. Pattern Anal. Mach. Intell. 30, 299–314 (2008). https://doi.org/10.1109/TPAMI.2007.1176

    Article  Google Scholar 

  16. Zhang, K., Zuo, W., Chen, Y., Meng, D., Zhang, L.: Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Trans. Image Process. 26, 3142–3155 (2016). https://doi.org/10.1109/TIP.2017.2662206

    Article  MathSciNet  MATH  Google Scholar 

  17. Santhanam, V., Morariu, V.I., Davis, L.S.: Generalized deep image to image regression. In: Proc. 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, pp. 5395–5405 (2017). https://doi.org/10.1109/CVPR.2017.573

  18. Mao, X.J., Shen, C., Yang, B.: Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections. Adv. Neural Inf. Process. Syst. 2810–2818 (2016). https://doi.org/10.48550/arxiv.1603.09056

  19. Zhang, Y., Li, K., Li, K., Zhong, B., Fu, Y.: Residual non-local attention networks for image restoration. In: 7th Int. Conf. Learn. Represent. ICLR 2019 (2019). https://doi.org/10.48550/arxiv.1903.10082

  20. Chen, J., Chen, J., Chao, H., Yang, M.: Image blind denoising with generative adversarial network based noise modeling. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 3155–3164 (2018). https://doi.org/10.1109/CVPR.2018.00333

  21. Pradhan, P., Guo, S., Ryabchykov, O., Popp, J., Bocklitz, T.W.: Deep learning a boon for biophotonics? J. Biophoton. 13, e201960186 (2020). https://doi.org/10.1002/JBIO.201960186

    Article  Google Scholar 

  22. Berisha, S., et al.: Deep learning for FTIR histology: leveraging spatial and spectral features with convolutional neural networks. Analyst 144, 1642–1653 (2019)

    Article  Google Scholar 

  23. Bhargava, R.: Towards a practical Fourier transform infrared chemical imaging protocol for cancer histopathology. Anal. Bioanal. Chem. 389, 1155–1169 (2007). https://doi.org/10.1007/s00216-007-1511-9

    Article  Google Scholar 

  24. Reddy, R.K., Bhargava, R.: Accurate histopathology from low signal-to-noise ratio spectroscopic imaging data. Analyst 135, 2818–2825 (2010)

    Article  Google Scholar 

  25. Koziol, P., et al.: Denoising influence on discrete frequency classification results for quantum cascade laser based infrared microscopy. Anal. Chim. Acta 1051, 24–31 (2019). https://doi.org/10.1016/J.ACA.2018.11.032

    Article  Google Scholar 

  26. Falahkheirkhah, K., Yeh, K., Mittal, S., Pfister, L., Bhargava, R.: Deep learning-based protocols to enhance infrared imaging systems. Chemom. Intell. Lab. Syst. 217, 104390 (2021). https://doi.org/10.1016/J.CHEMOLAB.2021.104390

    Article  Google Scholar 

  27. Bhargava, R., Wang, S.Q., Koenig, J.L.: Route to higher fidelity FT-IR imaging. Appl. Spectrosc. 54, 486–495 (2000). https://doi.org/10.1366/0003702001949898

    Article  Google Scholar 

  28. Bhargava, R., Wang, S.-Q., Koenig, J.L.: Processing FT-IR imaging data for morphology visualization. Appl. Spectrosc. 54, 1690–1706 (2000)

    Article  Google Scholar 

  29. Koziol, P., et al.: Comparison of spectral and spatial denoising techniques in the context of high definition FT-IR imaging hyperspectral data. Sci. Rep. 8, 1–11 (2018). https://doi.org/10.1038/s41598-018-32713-7

    Article  Google Scholar 

  30. Wang, T., Sun, M., Hu, K.: Dilated deep residual network for image denoising. In: Proc. Int. Conf. Tools with Artif. Intell. ICTAI, pp. 1272–1279 (2017). https://doi.org/10.48550/arxiv.1708.05473

  31. Luo, W., Li, Y., Urtasun, R., Zemel, R.: Understanding the effective receptive field in deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 4905–4913 (2017). https://doi.org/10.48550/arxiv.1701.04128

  32. Li, X., Li, F., Fern, X., Raich, R.: Filter shaping for convolutional neural networks. In: Int. Conf. Learn. Represent. (2017)

    Google Scholar 

  33. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  34. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  35. Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: Enhanced deep residual networks for single image super-resolution. In: IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work, pp. 1132–1140 (2017). https://doi.org/10.48550/arxiv.1707.02921

  36. Mittal, S., Yeh, K., Suzanne Leslie, L., Kenkel, S., Kajdacsy-Balla, A., Bhargava, R.: Simultaneous cancer and tumor microenvironment subtyping using confocal infrared microscopy for all-digital molecular histopathology. Proc. Natl. Acad. Sci. U.S.A. 115, 651–660 (2018). https://doi.org/10.1073/pnas.1719551115

    Article  Google Scholar 

  37. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations (ICLR) (2015)

    Google Scholar 

  38. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proc. Thirteen. Int. Conf. Artif. Intell. Stat., pp. 249–256 (2010)

    Google Scholar 

  39. Krull, A., Buchholz, T.O., Jug, F.: Noise2Void – learning denoising from single noisy images. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 2124–2132 (2018). https://doi.org/10.48550/arxiv.1811.10980

  40. Huang, T., Li, S., Jia, X., Lu, H., Liu, J.: Neighbor2Neighbor: self-supervised denoising from single noisy images. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 14776–14785 (2021). https://doi.org/10.48550/arxiv.2101.02824

  41. Moran, N., Schmidt, D., Zhong, Y., Coady, P.: Noisier2Noise: learning to denoise from unpaired noisy data. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 12061–12069 (2019). https://doi.org/10.1109/CVPR42600.2020.01208

  42. Pang, T., Zheng, H., Quan, Y., Ji, H.: Recorrupted-to-recorrupted: unsupervised deep learning for image denoising. In: Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 2043–2052 (2021). https://doi.org/10.1109/CVPR46437.2021.00208

Download references

Author information

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, Urbana, IL, 61801, USA

    Kianoush Falahkheirkhah & Rohit Bhargava

  2. Beckman Institute for Advanced Science and Technology, Urbana, IL, 61801, USA

    Kianoush Falahkheirkhah, Kevin Yeh, Matthew P. Confer & Rohit Bhargava

  3. Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA

    Rohit Bhargava

Authors
  1. Kianoush Falahkheirkhah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kevin Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthew P. Confer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rohit Bhargava
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rohit Bhargava .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. University of Illinois Urbana-Champaign, Urbana, IL, USA

    Bo Li

  3. National University of Singapore, Singapore, Singapore

    Angela Yao

  4. Microsoft Research Asia, Beijing, China

    Yang Liu

  5. University of Missouri, Columbia, MO, USA

    Ye Duan

  6. City University of Hong Kong, Kowloon, Hong Kong

    Manfred Lau

  7. Idaho National Laboratory, Idaho Falls, ID, USA

    Rajiv Khadka

  8. Salesforce, Seattle, WA, USA

    Ana Crisan

  9. Tufts University, Medford, MA, USA

    Remco Chang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Falahkheirkhah, K., Yeh, K., Confer, M.P., Bhargava, R. (2022). DRB-Net: Dilated Residual Block Network for Infrared Image Restoration. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13599. Springer, Cham. https://doi.org/10.1007/978-3-031-20716-7_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20716-7_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20715-0

  • Online ISBN: 978-3-031-20716-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics