Underwater optical image enhancement based on super-resolution convolutional neural network and perceptual fusion

Abstract

Underwater optical images often have serious quality degradations and distortions, which hinders the development of underwater optics and vision systems. Currently, there are two mainstream solutions: non-learning based and learning-based. Both have their advantages and disadvantages. To fully integrate the advantages of both, we propose an enhancement method based on superresolution convolutional neural network (SRCNN) and perceptual fusion. First, we introduce a weighted fusion BL estimation model with a saturation correction factor (SCF-BLs fusion), the accuracy of image prior information is improved effectively. Next, a refined underwater dark channel prior (RUDCP) is proposed, which combines guided filtering and an adaptive reverse saturation map (ARSM) to restore the image, which not only preserves edge details but also avoids the interference of artificial light. Then, the SRCNN fusion adaptive contrast enhancement is proposed to enhance the colour and contrast. Finally, to further enhance image quality, we employ efficient perceptual fusion to blend the different resulting outputs. Extensive experiments demonstrate that our method has outstanding visual results in underwater optical image dehazing, color enhancement and is artefact- and halo-free.