Single-image Dehazing using Detail Enhancement and Image Fusion
Tóm tắt: 63
| 
PDF: 81 
##plugins.themes.academic_pro.article.main##
Author
Từ khóa:
image dehazing
multi-exposed
detail enhancement
image fusion
dark channel prior
Tóm tắt
Haze is the suspension of atmospheric particles which is sufficient to reduce the visibility. Image dehazing refers to the processing tasks that lessen this negative effect. In this work, an alternative approach to single-image dehazing is developed which skips solving the haze formation equation, while still respects its hypothesis. In this method, we generated multiple under-exposed images from a single hazy input, followed by a detail enhancement process. Such resulting images were then merged using weights calculated based on the Dark Channel Prior assumption and overcame luminance enhancement. The visual improvement has been validated by both qualitative and quantitative evaluations.
Tài liệu tham khảo
-
[1] J.-H. Kim, C. Lee, J.-Y. Sim, and C.-S. Kim, “Single-image deraining using an adaptive nonlocal means filter,” in 2013 IEEE International Conference on Image Processing, 2013, pp. 914–917.
[2] Y. Luo, Y. Xu, and H. Ji, “Removing rain from a single image via discriminative sparse coding,” in 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 3397–3405.
[3] Y. Wang, C. Chen, S. Zhu, and B. Zeng, “A framework of single-image deraining method based on analysis of rain characteristics,” in 2016 IEEE International Conference on Image Processing (ICIP), 2016, pp. 4087–4091.
[4] F. Albu, C. Vertan, C. Florea, and A. Drimbarean, “One scan shadow compensation and visual enhancement of color images,” in 2009 16th IEEE International Conference on Image Processing (ICIP), 2009, pp. 3133–3136.
[5] S.-C. Huang, F.-C. Cheng, and Y.-S. Chiu, “Efficient contrast enhancement using adaptive gamma correction with weighting distribution,” IEEE Transactions on Image Processing, vol. 22, no. 3, pp. 1032–1041, 2013.
[6] D. Ngo, L. Seungmin, and B. Kang, “Light stretch algorithm for image quality enhancement,” 02 2018, pp. 56–60.
[7] K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 1956–1963.
[8] Q. Zhu, J. Mai, and L. Shao, “A fast single image haze removal algorithm using color attenuation prior,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3522–3533, 2015.
[9] J.-P. Tarel, N. Hautière, A. Cord, D. Gruyer, and H. Halmaoui, “Improved visibility of road scene images under heterogeneous fog,” in 2010 IEEE Intelligent Vehicles Symposium, 2010, pp. 478–485.
[10] K. Tang, J. Yang, and J. Wang, “Investigating haze-relevant features in a learning framework for image dehazing,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2995–3002.
[11] B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao, “Dehazenet: An end-to-end system for single image haze removal,” IEEE Transactions on Image Processing, vol. 25, no. 11, pp. 5187–5198, 2016.
[12] W. Ren, S. Liu, H. Zhang, J. Pan, X. Cao, and M.-H. Yang, “Single image dehazing via multi-scale convolutional neural networks,” in Computer Vision – ECCV 2016, 2016, pp. 154–169.
[13] C.-S. Fuh and T.-C. Tung, “Mcpa: A fast single image haze removal method based on the minimum channel and patchless approach,” IEEE Access, vol. 10, pp. 73 033–73 045, 2022.
[14] D. Berman, T. Treibitz, and S. Avidan, “Non-local image dehazing,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1674–1682.
[15] Y. Li, S. You, M. S. Brown, and R. T. Tan, “Haze visibility enhancement: A survey and quantitative benchmarking,” Computer Vision and Image Understanding, vol. 165, pp. 1–16, 2017.
[16] A. Galdran, “Image dehazing by artificial multiple-exposure image fusion,” Signal Processing, vol. 149, pp. 135–147, 2018.
[17] G.-J. KIM, L. Seungmin, and B. KANG, “Single image haze removal using hazy particle maps,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. E101.A, pp. 1999–2002, 11 2018.
[18] D. Park, H. Park, D. K. Han, and H. Ko, “Single image dehazing with image entropy and information fidelity,” in 2014 IEEE International Conference on Image Processing (ICIP), 2014, pp. 4037–4041.
[19] C. O. Ancuti, C. Ancuti, R. Timofte, and C. De Vleeschouwer, “O-haze: A dehazing benchmark with real hazy and haze-free outdoor images,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018, pp. 867–8678.
[20] C. Ancuti, A. Codruta, R. Timofte, and C. Vleeschouwer, I-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Indoor Images: 19th International Conference, ACIVS 2018, Poitiers, France, September 24–27, 2018, Proceedings, 09 2018, pp. 620–631.
[21] Z.Wang, A. Bovik, H. Sheikh, and E. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004.
[22] H. Yeganeh and Z. Wang, “Objective quality assessment of tone-mapped images,” IEEE Transactions on Image Processing, vol. 22, no. 2, pp. 657–667, 2013.
[23] L. Zhang, L. Zhang, X. Mou, and D. Zhang, “Fsim: A feature similarity index for image quality assessment,” IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2378–2386, 2011.
[24] L. K. Choi, J. You, and A. C. Bovik, “Referenceless prediction of perceptual fog density and perceptual image defogging,” IEEE Transactions on Image Processing, vol. 24, no. 11, pp. 3888–3901, 2015.
Xem thêm
##plugins.themes.academic_pro.article.sidebar##
Đã Xuất bản
Dec 31, 2022
Download
Cách trích dẫn
D. Hien, N., N. V. Tho, N. Q. Hieu, N. H.H. Cuong, T. T.M. Hanh, và T. H. Vu. “Single-Image Dehazing Using Detail Enhancement and Image Fusion”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 20, số p.h 12.2, Tháng Chạp 2022, tr 25-30, doi:10.31130/ud-jst.2022.561ICT.
