An Underwater Image Enhancement Method Based on Balanced Adaption Compensation
Wenjia Ouyang, Junnan Liu, Yanhui Wei
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.