Reducing the dynamic range of infrared images based on block-priority equalization and compression of histograms

Objectives. The problem of reducing the dynamic range of infrared images for their reproduction on display devices with a narrow dynamic range is considered. The method of local image histogram equalization based on the integral distribution function of brightness is investigated. To transform the brightness of a pixel, this method uses an approximation of the local alignment values of the nearest blocks of pixels of original image. This in-creases the local contrast of the image, but leads to high computational complexity, which is increasing while block size decreases. The aim of the work is to reduce the computational complexity of adaptive equalization and compression of infrared image histograms while reducing their dynamic range.

Methods. Image processing methods are used.

Results. To reduce the computational complexity of transforming the dynamic range of infrared images, a block-priority modification of the adaptive histogram equalization method is proposed. The modification is based on the division of the set of image blocks into two subsets of high-priority and low-priority blocks depend-ing on their brightness statistical properties. When interpolating pixel values, high-priority blocks use local alignment values, and low-priority blocks use global alignment values. As a result, the total number of alignment vectors is reduced in proportion to the ratio of subsets sizes and the computational complexity of the dynamic range transformation is reduced.

Conclusion. When changing the ratio of the number of high-priority blocks of infrared image pixels to the number of all blocks in the range of 0.25–0.75, the proposed algorithm is more efficient than global and adaptive histogram equalization algorithms.

1. Garcia F., Schockaert C., Mirbach B. Noise removal and real-time detail enhancement of high-dynamic-range infrared images with time consistency. International Conference on Quality Control by Artificial Vision, SPIE Proceedings, Le Creusot, France, 3 June 2015. Le Creusot, 2015, vol. 9534. https://doi.org/10.1117/ 12.2182896

2. Yang K.-F., Zhang X.-S., Li Y.-J. A biological vision inspired framework for image enhancement in poor visibility conditions. IEEE Transactions on Image Processing, 2020, vol. 29, pp. 1493–1506. https://doi.org/ 10.1109/tip.2019.2938310

3. Starovoitov V. V. Adaptive compressing of the high dynamic range of digital radar satellite images. Informatika [Informatics], 2018, no. 15(1), pp. 81–91 (In Russ.).

4. Lee J. W., Park R., Chang S. Local tone mapping using K-means algorithm and automatic gamma setting. IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, USA, 2011, pp. 807–808. https://doi.org/10.1109/ICCE.2011.5722876

5. Iwahashi M., Kiya H. Two layer lossless coding of HDR images. IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, BC, Canada, 2013, pp. 1340–1344. https://doi.org/ 10.1109/ICASSP.2013.6637869

6. Khan I. R., Aziz W., Shim S.-O. Tone-mapping using perceptual-quantizer and image histogram. IEEE Access, 2020, vol. 8, pp. 31350–31358. https://doi.org/10.1109/ACCESS.2020.2973273

7. Narwaria M., Da Silva M. P., Le Callet P., Pepion R. Adaptive contrast adjustment for postprocessing of tone mapped high dynamic range images. IEEE International Symposium on Circuits and Systems (ISCAS). Beijing, China, 2013, pp. 1103–1106. https://doi.org/10.1109/ISCAS.2013.6572043

8. Thai B. C., Mokraoui A., Matei B. HDR image tone mapping approach based on near optimal separable adaptive lifting scheme. Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). Poznan, Poland, 2018, pp. 108–113. https://doi.org/10.23919/SPA.2018.8563293

9. Huang P., Su Z., Li Z. Multi-scale bilateral grid for image tone mapping. International Conference on Multimedia Technology. Hangzhou, 2011, pp. 3143–3146. https://doi.org/10.1109/ICMT.2011.6003057

10. Huang C.-C., Ismail, Cai M.-X., Vu H. T. HDR compression based on image matting Laplacian. IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW). Nantou, Taiwan, 2016, pp. 1–2. https://doi.org/10.1109/ICCE-TW.2016.7520957

11. Chen Q., Liu X., Ran H., Dong S., Cui D., Deng X., Wang J. A fast multi-scale decomposition based tone mapping algorithm for High Dynamic Range images. IEEE International Conference on Systems, Man, and Cybernetics (SMC). Budapest, 2016, pp. 001455–001460. https://doi.org/10.1109/SMC.2016.7844442

12. Liu W., Wang Q., Liu Y., Li N. High dynamic tone mapping algorithm based on wavelet domain image fusion. 13th IEEE Conference on Industrial Electronics and Applications (ICIEA). Wuhan, China, 2018, pp. 1945–1950. https://doi.org/10.1109/ICIEA.2018.8398027

13. Lin Y., Huang M., Wang C. High dynamic range image composition using a linear interpolation approach. IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS). Okayama, Japan, 2016, pp. 1–6. https://doi.org/10.1109/ICIS.2016.7550796

14. Goswami A., Petrovich M., Hauser W., Dufaux F. Tone mapping operators: progressing towards semantic-awareness. IEEE International Conference on Multimedia & Expo Workshops (ICMEW). London, UK, 2020, pp. 1–6. https://doi.org/10.1109/ICMEW46912.2020.9106057

15. Lee J. W., Park R., Chang S. Local tone mapping using K-means algorithm and automatic gamma setting. IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, USA, 2011, pp. 807–808. https://doi.org/10.1109/ICCE.2011.5722876

16. Guangjun Z., Yan L. An improved tone mapping algorithm for High Dynamic Range images. International Conference on Computer Application and System Modeling (ICCASM 2010). Taiyuan, 2010, pp. V2-466–V2-468. https://doi.org/10.1109/ICCASM.2010.5620562

17. Baniс N., Lonсariс S. Puma: A high-quality retinex-based tone mapping operator. 24th European Signal Processing Conference (EUSIPCO). Budapest, Hungary, 2016, pp. 943–947. https://doi.org/10.1109/EUSIPCO. 2016.7760387

18. Cao X., Lai K., Yanushkevich S. N., Smith M. R. Adversarial and Adaptive Tone Mapping Operator for High Dynamic Range Images. IEEE Symposium Series on Computational Intelligence (SSCI). Canberra, Australia, 2020, pp. 1814–1821. https://doi.org/10.1109/SSCI47803.2020.9308535

19. Kumar N. A. M., Ravishankar B. S., Patil C. R. Real-time implementation of a novel detail enhancement algorithm for thermal imager. IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics Engineering (UPCON). Varanasi, India, 2016, pp. 1–6. https://doi.org/10.1109/UPCON.2016. 7894614.

20. Peng Y., Yan Y., Zhao J. Detail enhancement for infrared images based on propagated image filter. Mathematical Problems in Engineering, 2016, vol. 2016, pp. 1–12. https://doi.org/10.1155/2016/9410368

21. Zuo C., Chen Q., Liu N., Ren J., Sui X. Display and detail enhancement for high-dynamic-range infrared images. Optical Engineering, 2011, vol. 50(12), pp. 127401-1-10. https://doi.org/10.1117/1.3659698

22. Chen F., Zhang J., Cai J., Xu T., Lu G., Peng X. Infrared image adaptive enhancement guided by energy of gradient transformation and multiscale image fusion. Applied Sciences, 2020, vol. 10, pp. 1–21. https://doi.org/ 10.3390/app10186262

23. Kim T. K., Paik J. K., Kang B. S. Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Transactions on Consumer Electronics, 1998, vol. 44, no. 1, pp. 82–87. https://doi.org/10.1109/30.663733

24. Nithyananda C. R., Ramachandra A. C. Review on histogram equalization based image enhancement techniques. International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). Chennai, 2016, pp. 2512–2517.

25. Mante V., Frazor R. A., Bonin V., Geisler W. S., Carandini M. Independence of luminance and contrast in natural scenes and in the early visual system. Nature Neuroscience, 2005, vol. 8, pp. 1690–1697. https://doi.org/10.1038/nn1556

26. Wang Z., Simoncelli E. P., Bovik A. C. Multiscale structural similarity for image quality assessment. The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers. Pacific Grove, CA, USA, 2003, vol. 2, pp. 1398–1402. https://doi.org/10.1109/ACSSC.2003.1292216

27. Yeganeh H., Wang Z. Objective quality assessment of tone-mapped images. IEEE Transactions on Image Processing, 2013, vol. 22, no. 2, pp. 657–667. https://doi.org/10.1109/TIP.2012.2221725