An Underwater Image Enhancement Method Based on Balanced Adaption Compensation

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Abstract
Compensation for underwater optical image enhancement has achieved good performance by reducing the discrepancies among attenuated channels. Physical-based compensation approaches usually employ non-convex optimization, which results in ill-posed compensation in practice. However, the enhancement results of learning-based methods mostly tend to a certain pattern of synthesized data. In this paper, a balanced adaption compensation (BAC), leveraging the difference between learning-based and physical-based compensation applicabilities, is proposed to adapt the performance of data-driven semantic transfer and scene-relevant reconstruction. The adaptation of BAC breaks the limitation of scene diversity on compensation and reduces the dependence of the trained network on training patterns. Moreover, BAC is a perceptual enhancement method, that preserves the texture information of targets to ensure that enhanced results of BAC suit high-level visual tasks. Extensive qualitative and quantitative evaluations of underwater image enhancement with BAC show notable performance improvement against other compensation methods.
Funding Information
  • Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City (2021CXLH0001, 2021JJLH0003)
  • Key Research and Development Program of the Ministry of Science and Technology of China (2020YFC1512200)

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