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.

The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an underwater image enhancement optimization (MSR-PO) algorithm which uses the non-reference image quality assessment (NR-IQA) index as the optimization index. First of all, in a large number of experiments, we choose the Natural Image Quality Evaluator (NIQE) as the NR-IQA index and determine the appropriate parameters in MSR as the optimization object. Then, we use the Gravitational Search Algorithm (GSA) to optimize the underwater image enhancement algorithm based on MSR and the NIQE index. The experimental results show that this algorithm has an excellent adaptive ability to environmental changes.

Due to the scattering and absorption of light, underwater images face challenges such as color cast, low contrast, and blur. Existing deep learning-based Underwater Image Enhancement (UIE) algorithms often struggle to balance task requirements, enhanced image quality, and computing speed. To address these issues, this paper proposes a task-friendly framework suitable for simultaneous enhancement and super-resolution of underwater scenes. Firstly, it classifies underwater image illumination to reduce network burden and enhance network performance through functional decomposition. Secondly, it introduces a Redundant Feature-processing Module based on Dual-branch (RFMD), designed according to underwater imaging characteristics, and employs an external network to generate latent codes for reprocessing redundant features. RFMD effectively balances texture, color, contrast, and saturation across different tasks. Thirdly, a Sparse Redundant Feature-processing Module based on Dual-branch (SRFMD) is proposed, which significantly improves the network’s generalization ability and computing speed. Experimental data demonstrates that RFMD and SRFMD outperform traditional methods (IBLA and RGHS) and learning-based methods (Ucolor, FUnIE-GAN, FSRCNN, SRResNet, WDSR, ESRGAN, and URSCT-SESR) in balancing color, saturation, contrast, and texture information, as shown by both qualitative and quantitative evaluations. The code is publicly available at: https://github.com/ZHANGYW1/Class_RFMD.

In image sensing, measurements such as an object’s position or contour are typically obtained by analyzing digitized images. This method is widely used due to its simplicity. However, relative motion or inaccurate focus can cause motion and defocus blur, reducing measurement accuracy. Thus, video deblurring is essential. However, existing deep learning-based video deblurring methods struggle to balance high-quality deblurring, fast inference, and wide applicability. First, we propose a Current-Aware Temporal Fusion (CATF) framework, which focuses on the current frame in terms of both network architecture and modules. This reduces interference from unrelated features of neighboring frames and fully exploits current frame information, improving deblurring quality. Second, we introduce a Mixture-of-Experts module based on NAFBlocks (MoNAF), which adaptively selects expert structures according to the input features, reducing inference time. Third, we design a training strategy to support both sequential and temporally parallel inference. In sequential deblurring, we conduct experiments on the DVD, GoPro, and BSD datasets. Qualitative results show that our method effectively preserves image structures and fine details. Quantitative results further demonstrate that our method achieves clear advantages in terms of PSNR and SSIM. In particular, under the exposure setting of 3 ms–24 ms on the BSD dataset, our method achieves 33.09 dB PSNR and 0.9453 SSIM, indicating its effectiveness even in severely blurred scenarios. Meanwhile, our method achieves a good balance between deblurring quality and runtime efficiency. Moreover, the framework exhibits minimal error accumulation and performs effectively in temporal parallel computation. These results demonstrate that effective video deblurring serves as an important supporting technology for accurate image sensing.