Hybrid Adaptive Color Recovery (HACR): A Fundamental Pre-Processing Step for Image Restoration

References

[1] [1] C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, and M. Sbert, “Color Channel Compensation (3C): A Fundamental Pre-Processing Step for Image Enhancement,” IEEE Transactions on Image Processing, vol. 29, pp. 2653–2665, 2020.

[2] [2] K. He, J. Sun, and X. Tang, “Single Image Haze Removal Using Dark Channel Prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2341–2353, 2011.

[3] [3] T. Ojala, M. Pietikäinen, and D. Harwood, “Performance Evaluation of Texture Measures with Classification Based on Kullback Discrimination of Distributions,” Proceedings of the International Conference on Pattern Recognition, 1994.

[4] [4] G. D. Finlayson and E. Trezzi, “Shades of Gray and Colour Constancy,” Color and Imaging Conference, pp. 37–41, 2004.

[5] [5] J. van de Weijer, T. Gevers, and A. Gijsenij, “Edge-Based Color Constancy,” IEEE Transactions on Image Processing, vol. 16, no. 9, pp. 2207–2214, 2007.

[6] [6] C. Li, J. Guo, R. Cong, Y. Pang, and B. Wang, “Underwater Image Enhancement by Dehazing With Minimum Information Loss and Histogram Distribution Prior,” IEEE Transactions on Image Processing, vol. 25, no. 12, pp. 5664–5677, 2016.

[7] [7] C. Ancuti, C. O. Ancuti, T. Haber, and P. Bekaert, “Enhancing Underwater Images and Videos by Fusion,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2012.

[8] [8] X. Fu, D. Zeng, Y. Huang, X. Zhang, and X. Ding, “A Retinex-Based Enhancing Approach for Single Underwater Image,” IEEE International Conference on Image Processing, 2014.

[9] [9] P. Drews-Jr., E. Nascimento, F. Moraes, S. Botelho, and M. Campos, “Transmission Estimation in Underwater Images for Image Restoration,” IEEE Computer Graphics and Applications, vol. 36, no. 2, pp. 48–57, 2016.

[10] [10] S. Berman, T. Treibitz, and S. Avidan, “Non-Local Image Dehazing,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.

[11] [11] R. Fattal, “Dehazing Using Color-Lines,” ACM Transactions on Graphics, vol. 34, no. 1, 2014.

[12] [12] D. Huang, C. Shen, M. Ardabilian, Y. Wang, and L. Chen, “Local Binary Patterns and Its Application to Facial Image Analysis: A Survey,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 41, no. 6, pp. 765–781, 2011.

[13] [13] T. Jabid, M. H. Kabir, and O. S. Chae, “Local Directional Pattern (LDP): A Robust Image Descriptor for Object Recognition,” IEEE International Conference on Advanced Video and Signal-Based Surveillance, 2010.

[14] [14] T. Chakraborti, B. McCane, S. Mills, and U. Pal, “LOOP Descriptor: Local Optimal-Oriented Pattern,” IEEE Signal Processing Letters, vol. 25, no. 5, pp. 635–639, 2018.

[15] [15] A. Gijsenij and T. Gevers, “Color Constancy Using Natural Image Statistics,” IEEE Conference on Computer Vision and Pattern Recognition, 2011.

[16] [16] D. Berman, T. Treibitz, and S. Avidan, “Diving into Haze-Lines: Color Restoration of Underwater Images,” Proceedings of the British Machine Vision Conference, 2017.

[17] [17] J. Chiang and Y. Chen, “Underwater Image Enhancement by Wavelength Compensation and Dehazing,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 1756–1769, 2012.

[18] [18] D. Jobson, Z. Rahman, and G. Woodell, “A Multiscale Retinex for Bridging the Gap Between Color Images and the Human Observation of Scenes,” IEEE Transactions on Image Processing, vol. 6, no. 7, pp. 965–976, 1997.

[19] [19] S. Bianco, C. Cusano, and R. Schettini, “Color Constancy Using CNNs,” IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015.

[20] [20] Y. Ming, G. Wang, and C. Fan, “Uniform Local Binary Pattern Based Texture-Edge Feature for Image Recognition,” PLOS ONE, vol. 10, no. 5, 2015.

@article{1715106,
      author = {I. Naga Padmaja, Lakshman Kumar, Prasad, Chaitanya Abhiram, Madhusudhan},
      title = {Hybrid Adaptive Color Recovery (HACR): A Fundamental Pre-Processing Step for Image Restoration},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {1056-1062},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715106.pdf},
      abstract = {Images captured in challenging environments such as haze, underwater scenes, or artificial lighting often suffer from color degradation. In many cases, one color channel becomes weak, leading to loss of important visual information. To address this problem, this paper proposes a preprocessing method called Hybrid Adaptive Color Recovery (HACR). The method analyzes the relationship between color channels and adaptively compensates the weakened channel to restore color balance. By acting as a preprocessing step, HACR helps improve the performance of image restoration techniques. Experimental results show that the proposed method produces more consistent colors and better visual quality compared to conventional preprocessing approaches.},
      keywords = {Image Restoration, Color Channel Recovery, Preprocessing, Degraded Images, Adaptive Color Compensation.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715106}
  }