Skip to main content
SpringerLink
Log in

Modified Iterative Shrinkage Thresholding Algorithm for Image De-blurring in Medical Imaging

  • Conference paper
  • First Online:
Third Congress on Intelligent Systems (CIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 608))

Included in the following conference series:

  • 313 Accesses

Abstract

Computer-assisted medical diagnosis has grown its popularity among researchers and practitioners due to its high applicability and cost-effective applications. The use of learning methods in facilitating the processing and analyses of Medical Images has become necessary. Image de-blurring problem in Medical Imaging applications is a challenging task to improve the quality of the images. The class of Iterative Shrinkage Thresholding Algorithms (ISTA) is considered for handling the image de-blurring problems. These algorithms use first-order information and are tempting due to their simplicity. However, they converge pretty slowly. In this work, first, a faster version of ISTA is proposed by utilizing Nestrove’s updating rule. Later, the proposed approach is used to solve the image de-blurring problems arising in Medical Imaging. A theoretical study is performed to verify the improvements in the convergence rate of the modified fast ISTA. Numerical experiments suggest that the presented method for the image de-blurring problem outperforms the baseline ISTA method.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lang J, Gang K, Zhang C (2022) Adjustable shrinkage-thresholding projection algorithm for compressed sensing magnetic resonance imaging. Magn Reson Imaging 86:74–85

    Article  Google Scholar 

  2. Hu Q, Hu S, Ma X, Zhang F, Fang J (2022) MRI image fusion based on optimized dictionary learning and binary map refining in gradient domain. Multimed Tools Appl 1–23

    Google Scholar 

  3. Dang N, Tiwari S, Khurana M, Arya KV (2021) Recent advancements in medical imaging: a machine learning approach. Machine learning for intelligent multimedia analytics. Studies in big data, vol 82. Springer, Singapore

    Google Scholar 

  4. Brody H (2013) Medical imaging. Nature 502(7473):S81–S81

    Article  Google Scholar 

  5. Sharma P, Goyal D, Tiwari N (2022) Brain tumor analysis and reconstruction using machine learning. In: Congress on intelligent systems. Springer, Singapore, pp 381–394

    Google Scholar 

  6. Nishiyama M, Hadid A, Takeshima H, Shotton J, Kozakaya T, Yamaguchi O (2010) Facial deblur inference using subspace analysis for recognition of blurred faces. IEEE Trans Pattern Anal Mach Intell 33(4):838–845

    Article  Google Scholar 

  7. Tzeng J, Liu CC, Nguyen TQ (2010) Contourlet domain multiband deblurring based on color correlation for fluid lens cameras. IEEE Trans Image Process 19(10):2659–2668

    Article  MathSciNet  MATH  Google Scholar 

  8. Karaca E, Tunga MA (2018) An interpolation-based texture and pattern preserving algorithm for inpainting color images. Expert Syst Appl 91:223–234

    Article  Google Scholar 

  9. Ma J, Le Dimet FX (2009) Deblurring from highly incomplete measurements for remote sensing. IEEE Trans Geosci Remote Sens 47(3):792–802

    Article  Google Scholar 

  10. Huang L, Xia Y, Ye T (2021) Effective blind image deblurring using matrix-variable optimization. IEEE Trans Image Process 30:4653–4666

    Article  MathSciNet  Google Scholar 

  11. Dong J, Roth S, Schiele B (2021) Learning spatially-variant MAP models for non-blind image deblurring. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4886–4895

    Google Scholar 

  12. Dong W, Zhang K, Zhu C, Xu G, Fei F, Tao S (2022) Efficient non-blind deconvolution method for large scale blurred image with hybrid regularizations. Optik 169630

    Google Scholar 

  13. Bjorck A (1996) Numerical methods for least squares problems. Soc Ind Appl Math

    Google Scholar 

  14. Hansen PC, Nagy JG, O’leary DP (2006) Deblurring images: matrices, spectra, and filtering. Soc Ind Appl Math

    Google Scholar 

  15. Ben-Tal A, Nemirovski A (2001) Simple methods for extremely largescale problems. Lectures on modern convex optimization: analysis, algorithms, and engineering applications. SIAM, Philadelphia, PA, USA, pp 313–422

    Google Scholar 

  16. Elad M (2007) Iterative shrinkage algorithms. Sparse and redundant representations from theory to applications in signal and image processing. Springer, New York, NY, USA, pp 111–136

    Google Scholar 

  17. Daubechies I, Defrise M, De Mol C (2004) An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun Pure Appl Math 57(11):1413–1457

    Article  MathSciNet  MATH  Google Scholar 

  18. Bioucas-Dias JM, Figueiredo MAT (2007) A new TwIST: two-step iterative shrinkage/thresholding algorithms for image restoration. IEEE Trans Image Process 16(12):2992–3004

    Article  MathSciNet  Google Scholar 

  19. Beck A, Teboulle M (2009) A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202

    Article  MathSciNet  MATH  Google Scholar 

  20. Beck A, Teboulle M (2009) Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans Image Process 18(11):2419–2434

    Article  MathSciNet  MATH  Google Scholar 

  21. Bhotto MZA, Ahmad MO, Swamy MNS (2015) An improved fast iterative shrinkage thresholding algorithm for image deblurring. SIAM J Imaging Sci 8(3):1640–1657

    Article  MathSciNet  MATH  Google Scholar 

  22. Tianchai P (2021) An improved fast iterative shrinkage thresholding algorithm with an error for image deblurring problem. Fixed Point Theory Algorithms Sci Eng 1:1–25

    MathSciNet  MATH  Google Scholar 

  23. Chambolle A, De Vore RA, Lee N-Y, Lucier BJ (1998) Non-linear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage. IEEE Trans Image Process 7(3):319–335

    Article  MathSciNet  MATH  Google Scholar 

  24. Elad M, Matalon B, Zibulevsky M (2007) Coordinate and subspace optimization methods for linear least squares with non-quadratic regularization. Appl Comput Harmon Anal 23(3):346–367

    Article  MathSciNet  MATH  Google Scholar 

  25. Nesterov YE (1983) A method for solving the convex programming problem with convergence rate \(O(1/k^2)\). Dokl. akad. nauk Sssr 269:543–547

    MathSciNet  Google Scholar 

  26. Chambolle A, Dossal C (2015) On the convergence of the iterates of the “fast iterative shrinkage/thresholding algorithm’’. J Optim Theory Appl 166(3):968–982

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology, Mandi, 175005, India

    Himanshu Choudhary, Kartik Sahoo & Arishi Orra

Authors
  1. Himanshu Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kartik Sahoo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arishi Orra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Himanshu Choudhary .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    Sandeep Kumar

  2. Department of Computer Science and Engineering, Rajasthan Technical University, Kota, Rajasthan, India

    Harish Sharma

  3. Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bengaluru, India

    K. Balachandran

  4. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, Korea (Republic of)

    Joong Hoon Kim

  5. South Asian University, New Delhi, India

    Jagdish Chand Bansal

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Choudhary, H., Sahoo, K., Orra, A. (2023). Modified Iterative Shrinkage Thresholding Algorithm for Image De-blurring in Medical Imaging. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 608. Springer, Singapore. https://doi.org/10.1007/978-981-19-9225-4_35

Download citation

Publish with us

Policies and ethics

Search

Navigation