Underwater Image Enhancement Using Color Compensation and Multi-Scale Fusion with Quality Evaluation Metrics

References

[1] C. O. Ancuti, C. Ancuti, C. De Vleeschouwer, and P. Bekaert, “Color balance and fusion for underwater image enhancement,” *IEEE Transactions on Image Processing*, vol. 27, no. 1, pp. 379–393, 2018.

[2] G. L. Foresti, “Visual inspection of sea bottom structures by an autonomous underwater vehicle,” *IEEE Transactions on Systems, Man, and Cybernetics*, vol. 31, no. 5, pp. 691–705, 2001.

[3] A. Ortiz, M. Simó, and G. Oliver, “A vision system for an underwater cable tracker,” *Machine Vision and Applications*, vol. 13, pp. 129–140, 2002.

[4] A. Olmos and E. Trucco, “Detecting man-made objects in unconstrained subsea videos,” in *Proc. British Machine Vision Conference*, 2002.

[5] B. A. Levedahl and L. Silverberg, “Control of underwater vehicles in full unsteady flow,” *IEEE Journal of Oceanic Engineering*, vol. 34, no. 4, pp. 656–668, 2009.

[6] C. H. Mazel, “In situ measurement of reflectance and fluorescence spectra to support hyperspectral remote sensing and marine biology research,” in *Proc. IEEE OCEANS Conference*, 2006.

[7] Y. Kahanov and J. G. Royal, “Analysis of hull remains of the Dor D Vessel, Tantura Lagoon, Israel,” *International Journal of Nautical Archaeology*, vol. 30, pp. 257–265, 2001.

[8] R. Schettini and S. Corchs, “Underwater image processing: State of the art of restoration and image enhancement methods,” *EURASIP Journal on Advances in Signal Processing*, 2010.

[9] S. G. Narasimhan and S. K. Nayar, “Contrast restoration of weather degraded images,” *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 25, no. 6, pp. 713–724, 2003.

[10] D. He and G. G. L. Seet, “Divergent-beam LiDAR imaging in turbid water,” *Optics and Lasers in Engineering*, vol. 41, pp. 217–231, 2004.

[11] Y. Y. Schechner and Y. Averbuch, “Regularized image recovery in scattering media,” *IEEE Transactions on Pattern Analysis and Machine Intelligence*, vol. 29, no. 9, pp. 1655–1660, 2007.

[12] B. L. McGlamery, “A computer model for underwater camera systems,” *Proc. SPIE*, vol. 208, pp. 221–231, 1979.

[13] J. S. Jaffe, “Computer modeling and the design of optimal underwater imaging systems,” *IEEE Journal of Oceanic Engineering*, vol. 15, no. 2, pp. 101–111, 1990.

[14] K. He, J. Sun, and X. Tang, “Single image haze removal using dark channel prior,” in *Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2009.

[15] C. Ancuti and C. Ancuti, “Single image dehazing by multi-scale fusion,” *IEEE Transactions on Image Processing*, vol. 22, no. 8, pp. 3271–3282, 2013.

[16] 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.

[17] P. Drews, E. Nascimento, F. Moraes, S. Botelho, and M. Campos, “Transmission estimation in underwater single images,” in *Proc. IEEE International Conference on Computer Vision Workshops*, 2013.

[18] A. Galdran, D. Pardo, A. Picón, and A. Alvarez- Gila, “Automatic red-channel underwater image restoration,” *Journal of Visual Communication and Image Representation*, vol. 26, pp. 132–145, 2015.

[19] C. Ancuti, C. O. Ancuti, T. Haber, and P. Bekaert, “Enhancing underwater images and videos by fusion,” in *Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)*, 2012.

[20] G. D. Finlayson and E. Trezzi, “Shades of gray and colour constancy,” in *Proc. Color and Imaging Conference*, 2004.

[21] S. Wang, K. Ma, H. Yeganeh, Z. Wang, and W. Lin, “A patch structure representation method for quality assessment of contrast changed images,” IEEE Signal Process. Lett., vol. 22, no. 12,pp. 2387–2390, Dec. 2015

[22] M. Yang and A. Sowmya, “An underwater color image quality evaluation metric,” *IEEE Transactions on Image Processing*, vol. 24, no. 12, pp. 6062–6071, 2015.

[23] K. Panetta, C. Gao, and S. Ajayan, “Human visual system inspired underwater image quality measures,” *IEEE Journal of Oceanic Engineering*, vol. 41, no. 3, pp. 541–551, 2016.

@article{1715078,
      author = {Shakhamuri Karthik, Suryadevara Pavan, Pathigulla Sri Hari, Makkena Pardhu Vishnu Vardhan, Dr. A. Yaswanth Kumar},
      title = {Underwater Image Enhancement Using Color Compensation and Multi-Scale Fusion with Quality Evaluation Metrics},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {1041-1048},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715078.pdf},
      abstract = {Images acquired underwater are often characterized by poor contrast, visibility, and color information due to the absorption and scattering of light within the underwater environment. This affects the overall performance of underwater images, particularly in underwater exploration and robot navigation. This paper proposes an improved underwater image enhancement approach using color compensation and multi-scale fusion techniques. Initially, the red channel and white balancing are performed to compensate for the color information loss. Two improved images are formed using the gamma correction and detail enhancement approaches. Weight maps are calculated to focus on the salient features of the underwater images using the Laplacian contrast, saliency, and saturation features. Finally, the images are fused using the multi-scale approach based on the Gaussian and Laplacian pyramid operations. Post-processing operations are performed to enhance the overall visibility of the underwater images using CLAHE, contrast stretching, and saturation enhancement techniques.},
      keywords = {Underwater Image Enhancement, Color Compensation, Multi-Scale Fusion, Image Quality Metrics, Underwater Image Processing.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715078}
  }