Single Underwater Image Enhancement Using Integrated Variational Model

Underwater images often suffer from various degradation problems such as blurring, fog, low contrast, and color distortion because the light is absorbed and scattered when travelling through water. To solve these issues, we establish a novel framework combing variational methods and pyramid technology to improve image quality in the frequency domain. Two novel variational models, the adaptive variational contrast enhancement (AVCE) and total Laplacian model, are designed with the aim of enhancing the contrast of foreground and preserving texture features at different scales. In order to solve these two models efficiently, we also exploit two optimal algorithms based on gradient descent method (GDM) and alternating direction method of multipliers (ADMM). In addition, fast Fourier transform (FFT) is applied for further accelerating the calculation procedure. Extensive experiments demonstrate that our approach achieves a good performance on contrast enhancement, color correction, and texture enlargement for underwater images. Qualitative and quantitative comparisons with several state-of-the-art methods further validate the superiority of our proposed method