NPT-UL: An underwater image enhancement framework based on non-physical transformation and unsupervised learning

Underwater images often face visual degradation problems such as color deviations, low contrast, and blurred details. Previous studies suffer from poor visual perception performance and over-enhancement, which poses a challenge to improve the visibility of underwater images comprehensively and consistently. This paper proposes a framework for underwater image enhancement based on non-physical transformation and unsupervised learning (NPT-UL). The framework includes an image data augmentation strategy and an unsupervised learning model. Firstly, a data augmentation strategy based on non-physical transformation is proposed, which consists of three types of transformation processes to optimize the color deviation, contrast, and detail blur in the initial image data set. Secondly, an unsupervised learning model based on contrast learning and generative adversarial network is proposed to enhance the input underwater image. Three loss functions including adversarial loss, PatchNCE loss, and identity loss are consolidated into the network to ensure structural similarity and color authenticity. Finally, qualitative, quantitative, ablative experiments are presented to evaluate the performance using different underwater image datasets. Compared with the state-of-art methods, NPT-UL has better performance which can effectively improve the image visual quality. Tested in the UIEB data set, the values of UCIQE, CCF, UIQM, BC(e) and BC(r) metrics obtained by NPT-UL reach 0.644, 42.311, 4.325, 4.761 and 2.719, separately. The proposed framework can help to solve the problems of color distortion and feature loss, showing significant application potential in the field of underwater image restoration and target detection.