When Smart Signal Processing Meets Smart Imaging

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11010)


With the advancement of modern sensing and imaging technologies, people can acquire measurements more effectively and efficiently for data of various modalities. Meanwhile, the new imaging technologies also bring challenges for the corresponding signal processing systems, in order to achieve high-quality image reconstruction and rendering. To fully utilize the advanced imaging schemes, we need smart signal processing methodologies. In this paper, we will cover some recent trends on techniques for high dynamic range (HDR) imaging, compressed sensing, computational imaging, as well as the image recovery methods with data-driven regularizers. Related works and examples are presented, to illustrate new problems and challenges of signal processing in the context of modern sensing systems.


High dynamic range Compressed sensing Image processing Computational imaging Machine learning 


  1. 1.
    Avidan, S., Shamir, A.: Seam carving for content-aware image resizing. ACM Trans. Graph. (TOG) 26(3), 10 (2007)CrossRefGoogle Scholar
  2. 2.
    Beck, A., Teboulle, M.: Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18(11), 2419–2434 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Borer, T., Cotton, A.: A “display independent” high dynamic range television system. In: International Broadcasting Convention. IET (2015)Google Scholar
  4. 4.
    Boyd, S., Parikh, N., Chu, E., Peleato, B., Eckstein, J., et al.: Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3(1), 1–122 (2011)CrossRefGoogle Scholar
  5. 5.
    Burger, H.C., Schuler, C.J., Harmeling, S.: Image denoising: can plain neural networks compete with BM3D? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2392–2399. IEEE (2012)Google Scholar
  6. 6.
    Candes, E.J., Romberg, J.K., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207–1223 (2006)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Chang, S.G., Yu, B., Vetterli, M.: Adaptive wavelet thresholding for image denoising and compression. IEEE Trans. Image Process. (TIP) 9(9), 1532–1546 (2000)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. (TIP) 16(8), 2080–2095 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Donoho, D.L.: For most large underdetermined systems of linear equations the minimal \(\ell _1\)-norm solution is also the sparsest solution. Commun. Pure Appl. Math. 59(6), 797–829 (2006)Google Scholar
  11. 11.
    Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Froehlich, J., Su, G.M., Daly, S., Schilling, A., Eberhardt, B.: Content aware quantization: requantization of high dynamic range baseband signals based on visual masking by noise and texture. In: IEEE International Conference onImage Processing (ICIP), pp. 884–888. IEEE (2016)Google Scholar
  13. 13.
    Gleichman, S., Eldar, Y.C.: Blind compressed sensing. IEEE Trans. Inf. Theory 57(10), 6958–6975 (2011)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Gu, S., Xie, Q., Meng, D., Zuo, W., Feng, X., Zhang, L.: Weighted nuclear norm minimization and its applications to low level vision. Int. J. Comput. Vis. 121(2), 183–208 (2017)CrossRefGoogle Scholar
  15. 15.
    Kadu, H., Song, Q., Su, G.M.: Single layer progressive coding for high dynamic range video. In: Picture Coding Symposium (2018)Google Scholar
  16. 16.
    Liu, D., Wen, B., Liu, X., Huang, T.S.: When image denoising meets high-level vision tasks: a deep learning approach (2017)Google Scholar
  17. 17.
    Lu, T., et al.: ITP colour space and its compression performance for high dynamic range and wide colour gamut video distribution. ZTE Commun. 14(1), 32–38 (2016)Google Scholar
  18. 18.
    Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic resonance in medicine 58(6), 1182–1195 (2007)CrossRefGoogle Scholar
  19. 19.
    Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Non-local sparse models for image restoration. In: IEEE 12th International Conference on Computer Vision, ICCV, pp. 2272–2279 (2009)Google Scholar
  20. 20.
    Miller, S., Nezamabadi, M., Daly, S.: Perceptual signal coding for more efficient usage of bit codes. SMPTE Motion Imaging J. 122(4), 52–59 (2013)CrossRefGoogle Scholar
  21. 21.
    Nezamabadi, M., Miller, S., Daly, S., Atkins, R.: Color signal encoding for high dynamic range and wide color gamut based on human perception. In: Color Imaging XIX: Displaying, Processing, Hardcopy, and Applications, vol. 9015, p. 90150C. International Society for Optics and Photonics (2014)Google Scholar
  22. 22.
    Ravishankar, S., Bresler, Y.: MR image reconstruction from highly undersampled k-space data by dictionary learning. IEEE Trans. Med. Imaging 30(5), 1028–1041 (2011)CrossRefGoogle Scholar
  23. 23.
    Recommendation ITU-R BT2100-0: image parameter values for high dynamic range television for use in production and international programme exchange, July 2016Google Scholar
  24. 24.
    Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Phys. D: Nonlinear Phenom. 60(1–4), 259–268 (1992)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Ström, J., et al.: High quality HDR video compression using HEVC main 10 profile. In: Picture Coding Symposium (PCS), pp. 1–5. IEEE (2016)Google Scholar
  26. 26.
    Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. In: Proceedings of International Conference on Computer Vision (ICCV) (2017)Google Scholar
  27. 27.
    Wen, B., Harshad, K., Su, G.: Inverse Luma/Chroma mappings with histogram transfer and approximation, April 5 2018, US Patent App. 15/725, 101Google Scholar
  28. 28.
    Wen, B., Ravishankar, S., Bresler, Y.: Structured overcomplete sparsifying transform learning with convergence guarantees and applications. Int. J. Comput. Vis. 114(2), 137–167 (2015)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Wen, B., Li, Y., Bresler, Y.: When sparsity meets low-rankness: transformlearning with non-local low-rank constraint for image restoration. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2297–2301. IEEE (2017)Google Scholar
  30. 30.
    Wen, B., Li, Y., Pfister, L., Bresler, Y.: Joint adaptive sparsity and low-rankness on the fly: an online tensor reconstruction scheme for video denoising. In: IEEE International Conference on Computer Vision (ICCV), vol. 1 (2017)Google Scholar
  31. 31.
    Wen, B., Ravishankar, S., Bresler, Y.: FRIST- flipping and rotation invariant sparsifying transform learning and applications. Inverse Probl. 33(7), 074007 (2017)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Wen, B., Su, G.M.: Transim: transfer image local statistics across EOTFS for HDR image applications. In: International Conference on Multimedia and Expo (ICME). IEEE (2018)Google Scholar
  33. 33.
    Wong, C.W., Su, G.M., Wu, M.: Impact analysis of baseband quantizer on coding efficiency for HDR video. IEEE Signal Process. Lett. 23(10), 1354–1358 (2016)CrossRefGoogle Scholar
  34. 34.
    Yang, Y., Sun, J., Li, H., Xu, Z.: Deep ADMM-Net for compressive sensing MRI. In: Advances in Neural Information Processing Systems, pp. 10–18 (2016)Google Scholar
  35. 35.
    Yang, Y., Sun, J., Li, H., Xu, Z.: Deep-ADMM-Net. (2017). gitHub repository
  36. 36.
    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(7), 3142–3155 (2017)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Dolby LaboratoriesSunnyvaleUSA

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