Detailed and enhanced multi-exposure image fusion using recursive filter

Abstract

A single photo is usually inadequate to represent a high-quality scene due to the dynamic range limitation. A high-quality image can be obtained by fusing multi-exposure images of the same scene. However, ghosting artifact can be produced in the fused image due to moving objects. To overcome this problem, we propose a detailed and enhanced multi-exposure image fusion technique using an edge-preserving recursive filter. The proposed technique reduces the artifacts near edges and produces an HDR-like image without any ghosting artifact. The idea behind the proposed method is to first decompose the LDR multiple-exposed input images into the detail layer and the base layer to extract the sharp and fine details, respectively. To do so, first, the recursive filter is applied to input images. Then, these recursive-based output images are used for extracting the detail and base layer. Finally, the detail layer and the base layer are combined together to produce a detailed and enhanced image without artifacts. Additionally, the proposed method is suitable for multi-focus image fusion. Experimental results prove the effectiveness of the proposed method over the existing methods both qualitatively and quantitatively.

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References

  1. 1.

    Artusi A, Richter T, Ebrahimi T, Mantiuk RK (2017) High Dynamic Range Imaging Technology. IEEE Signal Process Mag 34(5):165—172

  2. 2.

    Banterle F, Artusi A, Debattista K, Chalmers A (2017) Advanced high dynamic range imaging. AK Peters/CRC Press

  3. 3.

    Biradar N, Dewal ML, Rohit MK (2014) Edge preserved speckle noise reduction using integrated fuzzy filters. International scholarly research notices

  4. 4.

    Codes- Scholar Homepage. http://www.escience.cn/people/liuyu1/Codes.html (accessed August 15, 2018)

  5. 5.

    Demirel H, Anbarjafari G (2011) IMAGE Resolution enhancement by using discrete and stationary wavelet decomposition. IEEE Trans Image Process 20:1458–1460

    MathSciNet  MATH  Article  Google Scholar 

  6. 6.

    Eilertsen G, Mantiuk RK, Unger J (2017) A Comparative Review of Tone Mapping Algorithms for High Dynamic Range Video. Comput Gr Forum 36:565—592

  7. 7.

    Gastal E S L, Oliveira M M (2011) Domain transform for edge-aware image and video processing. ACM Trans Graph 30(4):69

    Article  Google Scholar 

  8. 8.

    Gonzalea RC, Woods RE (2004) Wavelets and Multiresolution Processing Digital Image Processing. Prentice Hall

  9. 9.

    He K, Sun J, Tang X (2013) Guided image filtering. IEEE Trans. Pattern Anal Mach Intell 35(6): 1397—1409

  10. 10.

    Huang F, Zhou D, Nie R, Yu C (2018) A Color Multi-Exposure Image Fusion Approach Using Structural Patch Decomposition. IEEE Access 6:42877—42885

  11. 11.

    Huo Y Q, Zhang X D (2016) Single image-based HDR imaging with CRF estimation. Int. Conf. On Communication Problem-Solving (ICCP) (IEEE), pp 1–3

  12. 12.

    Huo Y, Zhang X (2017) Single image-based HDR image generation with camera response function estimation. Image Process. IET 11: 1317—1324

  13. 13.

    Jain P, Tyagi V. (2015) An adaptive edge-preserving image denoising using epsilon-median filtering in tetrolet domain. In: Emerging ICT for Bridging the Future-Proceedings of the 49th Annual Convention of the Computer Society of India (CSI), vol 1, pp 393–400

  14. 14.

    Jain P, Tyagi V (2016) A survey of edge-preserving image denoising methods. Inf Syst Front 18(1):159–170

    Article  Google Scholar 

  15. 15.

    Kalantari N K, Ramamoorthi R (2017) Deep high dynamic range imaging of dynamic scenes. ACM Trans Graph 36(4):144–1

    Article  Google Scholar 

  16. 16.

    Khan IR, Rahardja S, Khan MM, Movania MM, Abed F (2018) A tone-mapping technique based on histogram using a sensitivity model of the human visual system. IEEE Trans Ind Electron 65:3469—3479

  17. 17.

    Lee S H, Park J S, Cho N I (2018) A Multi-Exposure image fusion based on the adaptive weights reflecting the relative pixel intensity and global gradient. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp 1737–1741

  18. 18.

    Li R, Yu S, Yang X (2007) Efficient spatio-temporal segmentation for extracting moving objects in video sequences. IEEE Trans Consum Electron 53(3):1161—1167

  19. 19.

    Li S, Kang X (2012) Fast multi-exposure image fusion with median filter and recursive filter. IEEE Trans Consum Electron 58(2):626—632

  20. 20.

    Liu Y, Wang Z (2015) Dense SIFT for ghost-free multi-exposure fusion. J Vis Commun Image Represent 31:208–224

    Article  Google Scholar 

  21. 21.

    Li Z, Wei Z, Wen C, Zheng J (2017) Detail-Enhanced Multi-Scale Exposure Fusion. IEEE Trans Image Process 26:1243—1252

  22. 22.

    Ma K, Li H, Yong H, Wang Z, Meng D, Zhang L (2017) Robust Multi-Exposure image fusion: a structural patch decomposition approach. IEEE Trans Image Process 26(5):2519–2532

    MathSciNet  MATH  Article  Google Scholar 

  23. 23.

    Ma K https://ece.uwaterloo.ca/~k29ma/ (accessed August 15, 2018)

  24. 24.

    Ma K D, Zeng K, Wang Z (2015) Perceptual quality assessment for multiexposureimagefusion. IEEE Trans Image Process 24(11):3345–3356

    MathSciNet  MATH  Article  Google Scholar 

  25. 25.

    Mertens T, Kautz J, Van Reeth F (2007) Exposure fusion. IEEE Conf. on 15th Pacific Computer Graphics and Applications, Washington, pp 382—390

  26. 26.

    Mittal A, Moorthy A K, Bovik A C (2012) No-reference image quality assessment in the spatial domain. IEEE Trans Image Process 21(12):4695–708

    MathSciNet  MATH  Article  Google Scholar 

  27. 27.

    Mittal A, Soundararajan R, Bovik MAC (2013) A ’completely blind’ image quality analyzer. IEEE Signal Process Lett 20(3):209–212

    Article  Google Scholar 

  28. 28.

    Núñez J, Otazu X, Fors O, Prades A, Palà V, Arbiol R (1999) Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans Geosci Remote Sens 37:1204–1211

    Article  Google Scholar 

  29. 29.

    Paris S, Durand F (2006) A fast approximation of the bilateral filter using a signal processing approach. In: Proceedings of ECCV, pp 568–580

  30. 30.

    Paris S, Hasinoff S W, Kautz J (2011) Local laplacian filters: edge-aware image processing with a laplacian pyramid. ACM Trans Graph 30(4):68–1

    Article  Google Scholar 

  31. 31.

    Reinhard E, Ward G, Pattanaik S, Debevec P (2010) High dynamic range imaging: Acquisition, Display, and Image-Based Lighting. Morgan Kaufmann Publishers

  32. 32.

    Richard S (2016) High dynamic range imaging. Opt Eng 52:913—916

  33. 33.

    Serrano A, Heide F, Gutierrez D, Wetzstein G, Masia B (2016) Convolutional sparse coding for high dynamic range imaging. Comput Graph Forum 35 (2):153–163

    Article  Google Scholar 

  34. 34.

    Shen J, Zhao Y, Yan S, Li X (2014) Exposure fusion using boosting Laplacian pyramid. IEEE Trans Cybern 44(9):1579—1590

  35. 35.

    Vanmali A V, Kelkar S G, Gadre V M (2015) Multi-exposure image fusion for dynamic scenes without ghost effect. Proc. 21st Nat. Conf. Commun. (NCC), pp 1–6

  36. 36.

    Yan J, Li J, Fu X (2019) No-reference quality assessment of contrast-distorted images using contrast enhancement. arXiv:1904.08879

  37. 37.

    You X, Du L, Cheung Y-m, Chen Q (2010) A Blind Watermarking Scheme Using New Nontensor Product Wavelet Filter Banks. IEEE Trans Image Process 19(12):3271–3284

    MathSciNet  MATH  Article  Google Scholar 

  38. 38.

    Youm SJ, Cho WH, Hong KS (2005) High Dynamic Range Video through Fusion of Exposure-Controlled Frames. In: Proceedings of IAPR Conference on Machine VIsion Applications, pp 546—549

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Correspondence to Muhammad Imran.

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Hayat, N., Imran, M. Detailed and enhanced multi-exposure image fusion using recursive filter. Multimed Tools Appl (2020). https://doi.org/10.1007/s11042-020-09190-0

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Keywords

  • Multi-exposure
  • Low dynamic range
  • High dynamic range
  • Histogram
  • Recursive filter