Underwater video images, as the primary carriers of underwater information, play a vital role in human exploration and development of the ocean. Due to the optical characteristics of water bodies, underwater video images generally have problems such as color bias and unclear image quality, and image quality degradation is severe. Degenerated images have adverse effects on the visual tasks of underwater vehicles, such as recognition and detection. Therefore, it is vital to obtain high-quality underwater video images. Firstly, this paper analyzes the imaging principle of underwater images and the reasons for their decline in quality and briefly classifies various existing methods. Secondly, it focuses on the current popular deep learning technology in underwater image enhancement, and the underwater video enhancement technologies are also mentioned. It also introduces some standard underwater data sets, common video image evaluation indexes and underwater image specific indexes. Finally, this paper discusses possible future developments in this area.

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

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We present a new method to reconstruct high resolution SAR images. When the far-field data arranged in the polar format, a 2-D Fourier transform can achieve the image reconstruction. But the data do not fall on a Cartesian grid, we introduce 2-D Nonuniform Fast Fourier Transform (NUFFT) to reconstruct the SAR image directly. It is the first time to use NUFFT technology in SAR image reconstruction. Instead of interpolating the input data, NUFFT interpolate the exponential function for each sampling point. The technology could easily extended to high dimensional by tensor product. The 2-D NUFFT is very suitable to reconstruct SAR image. A ground target example will shown to demonstrate that the reconstructed images have higher resolution than the general method when using the same data.

At present, 3D reconstruction technology is being gradually applied to underwater scenes and has become a hot research direction that is vital to human ocean exploration and development. Due to the rapid development of computer vision in recent years, optical image 3D reconstruction has become the mainstream method. Therefore, this paper focuses on optical image 3D reconstruction methods in the underwater environment. However, due to the wide application of sonar in underwater 3D reconstruction, this paper also introduces and summarizes the underwater 3D reconstruction based on acoustic image and optical–acoustic image fusion methods. First, this paper uses the Citespace software to visually analyze the existing literature of underwater images and intuitively analyze the hotspots and key research directions in this field. Second, the particularity of underwater environments compared with conventional systems is introduced. Two scientific problems are emphasized by engineering problems encountered in optical image reconstruction: underwater image degradation and the calibration of underwater cameras. Then, in the main part of this paper, we focus on the underwater 3D reconstruction methods based on optical images, acoustic images and optical–acoustic image fusion, reviewing the literature and classifying the existing solutions. Finally, potential advancements in this field in the future are considered.