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GPU fast restoration of non-uniform illumination images

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Abstract

This paper presents a GPU based parallel implementation for the non-uniform illumination image restoration method, which uses a retinex based algorithm to decompose the original image into brightness and reflectance components, and adjusts the brightness value through an adaptive gamma correction and nonparametric mapping to achieve the restoration. Specifically, we parallelize the improved retinex algorithm on GPU to extract the brightness value of each pixel. After that, the probability of different brightness range is counted through each block to the entire image to reduce the competition of memory access. Finally, we use two different parallel reduce methods to calculate the probability density and cumulative density of brightness value and generate the mapping curve. The experiment conducted on three different GPUs and two CPUs with different resolution images shows that our method can process a 1024 × 2048 image in 1.024 ms on RTX2080Ti, indicates a great potential for real-time application.

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References

  1. Lee, S., et al.: A review on dark channel prior based image dehazing algorithms. Eurasip J. Image Video Process. 1, 4 (2016)

    Article  Google Scholar 

  2. Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)

  3. Kumar, A., et al.: Tchebichef moment based restoration of Gaussian blurred images. Appl. Opt. 55(32), 9006 (2016)

    Article  Google Scholar 

  4. Stoker, D.S., et al.: Restoration and recognition of distant, blurry irises. Appl. Opt. 52(9), 1864–1875 (2013)

    Article  Google Scholar 

  5. Ma, L., et al.: Region-confined restoration method for motion-blurred star image of the star sensor under dynamic conditions. Appl. Opt. 55(17), 4621 (2016)

    Article  Google Scholar 

  6. Solachidis, V., Maiorana, E., Campisi, P.: HDR image multi-bit watermarking using bilateral-filtering-based masking. In: Proceedings of SPIE (2013)

  7. Kim, K., Bae, J., Kim, J.: Natural HDR image tone mapping based on retinex. IEEE Trans. Consum. Electron. 57(4), 1807–1814 (2012)

    Article  Google Scholar 

  8. Khan, I.R., et al.: HDR image tone mapping using histogram adjustment adapted to human visual system. In: International Conference on Information (2009)

  9. Fang, C., Liao, Z., Yu, Y.: Piecewise flat embedding for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 99, 1 (2018)

    Google Scholar 

  10. Jie, T., et al.: A textured object recognition pipeline for color and depth image data. IEEE Int. Conf. Robot. Autom. 2012, 3467–3474 (2012)

    Google Scholar 

  11. Kong, Q.J., et al.: Efficient traffic state estimation for large-scale urban road networks. IEEE Trans. Intell. Transp. Syst. 14(1), 398–407 (2013)

    Article  Google Scholar 

  12. Wu, X., et al.: Separable convolution template (SCT) background prediction accelerated by CUDA for infrared small target detection. Infrared Phys. Technol. 60, 300–305 (2013)

    Article  Google Scholar 

  13. Yang, X., et al.: Implementing real-time RCF-Retinex image enhancement method using CUDA. J. Real Time Image Process. 16(1), 115–125 (2019)

    Article  Google Scholar 

  14. Haythem, B., et al.: Fast generalized fourier descriptor for object recognition of image using CUDA. In: Computer Applications & Research IEEE (2014)

  15. Yuan, Y., et al.: A fast single-image super-resolution method implemented with CUDA. J. Real Time Image Process. 16(1), 81–97 (2019)

    Article  Google Scholar 

  16. Jalloul, M., Baydoun, M., Al-Alaoui, M.A.: Gauss-Newton image registration with CUDA. In: IEEE International Conference on Electronics IEEE (2012)

  17. Haweel, R.T., El-Kilani, W.S., Ramadan, H.H.: Fast approximate DCT with GPU implementation for image compression. J. Vis. Commun. Image Represent. 40, 357–365 (2016)

    Article  Google Scholar 

  18. Lamas-Rodríguez, J., et al.: GPU-accelerated level-set segmentation. J. Real Time Image Process. 12(1), 15–29 (2016)

    Article  Google Scholar 

  19. Shin, Yonghun, Jeong, Soowoong, Lee, Sangkeun: Efficient naturalness restoration for non-uniform illumination images. IET Image Proc. 9(8), 662–671 (2015)

    Article  Google Scholar 

  20. Reza, Ali M.: Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. J. VLSI Signal Process. Syst. Signal Image Video Technol. 38(1), 35–44 (2004)

    Article  Google Scholar 

  21. Park, J.S., Cho, N.I.: Generation of high dynamic range illumination from a single image for the enhancement of undesirably illuminated images. arXiv:1708.00636

  22. Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., Chatterjee, J.: Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consum. Electron. 56(4), 2475–2480 (2010)

    Article  Google Scholar 

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Funding

National Science Foundation of China (61675160, 61705173, 51801142); Natural Science Foundation of Shaanxi Province (2018JQ5022, 2018JQ6004); Fundamental Research Funds for the Central University (JB180502, JBX170513); China Scholarship Council (201806960036) and 111 Project (B17035).

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Correspondence to Yue Yu.

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Cheng, K., Yu, Y., Zhou, H. et al. GPU fast restoration of non-uniform illumination images. J Real-Time Image Proc 18, 75–83 (2021). https://doi.org/10.1007/s11554-020-00950-7

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  • DOI: https://doi.org/10.1007/s11554-020-00950-7

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