Affiliation:
1. Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of Korea
Abstract
Underwater imaging presents unique challenges, notably color distortions and reduced contrast due to light attenuation and scattering. Most underwater image enhancement methods first use linear transformations for color compensation and then enhance the image. We observed that linear transformation for color compensation is not suitable for certain images. For such images, non-linear mapping is a better choice. This paper introduces a unique underwater image restoration approach leveraging a streamlined convolutional neural network (CNN) for dynamic weight learning for linear and non-linear mapping. In the first phase, a classifier is applied that classifies the input images as Type I or Type II. In the second phase, we use the Deep Line Model (DLM) for Type-I images and the Deep Curve Model (DCM) for Type-II images. For mapping an input image to an output image, the DLM creatively combines color compensation and contrast adjustment in a single step and uses deep lines for transformation, whereas the DCM employs higher-order curves. Both models utilize lightweight neural networks that learn per-pixel dynamic weights based on the input image’s characteristics. Comprehensive evaluations on benchmark datasets using metrics like peak signal-to-noise ratio (PSNR) and root mean square error (RMSE) affirm our method’s effectiveness in accurately restoring underwater images, outperforming existing techniques.
Funder
Education and Research Promotion Program of KOREATECH
Reference44 articles.
1. Akkaynak, D., Treibitz, T., Shlesinger, T., Loya, Y., Tamir, R., and Iluz, D. (2017, January 21–26). What is the space of attenuation coefficients in underwater computer vision?. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.
2. Underwater image restoration based on image blurriness and light absorption;Peng;IEEE Trans. Image Process.,2017
3. Single image haze removal using dark channel prior;He;IEEE Trans. Pattern Anal. Mach. Intell.,2010
4. Underwater image enhancement: A comprehensive review, recent trends, challenges and applications;Raveendran;Artif. Intell. Rev.,2021
5. Wu, H., Liu, J., Xie, Y., Qu, Y., and Ma, L. (2020, January 14–19). Knowledge transfer dehazing network for nonhomogeneous dehazing. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Virtual.