The underwater image quality is usually damaged due to the selective absorption of sea water and the scattering effect of particles, which leads to image distortion and will reduces the accuracy and efficiency of subsequent vision tasks. To solve the above problems, an underwater single image enhancement method based on latent low-rank decomposition and image fusion is proposed. First, a color correction method based on channel compensation is introduced to remove color cast. Second, an improved Laplace sharpening method and a gamma correction technology are applied to effectively improve the sharpness and contrast of the image. Then, the latent low-rank representation is utilized to decompose the obtained image. Finally, a dual-image weighted image fusion strategy is proposed to integrate the enhanced image. The experimental results show that the method can obtain better results than the traditional methods on both qualitative and quantitative analysis.
Underwater imaging has been increasingly employed in vision-based marine research. However, the inappropriate installation of a light source and the complex underwater environment will result in the uneven illumination and overexposure on the captured images. To address these issues, an underwater image enhancement framework for autonomous underwater vehicles platform is proposed, which consists of underwater light source optimization and illumination nonuniformity correction. The light source optimization method improves the imaging quality by computing an appropriate angle of the light casting. In this way, the center of the field of view is always well lit. In addition, an adaptive filter-based illumination correction algorithm is proposed to solve the uneven illumination caused by the artificial light source. During this process, image block segmentation and the measure of image enhancement index are applied to improve the adaptability and reduce the calculation errors of the filter parameters. A dataset with real underwater images collected under different natural conditions has been built and tested. The experimental results indicate that the proposed method is more adaptive and effective than the typical methods.
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