Images captured underwater usually suffer from color distortion, detail blurring, low contrast, and a bluish or greenish tone due to light scattering and absorption in the underwater medium, which in turn the visibility is adversely affected by these factors seriously. Over the last decades, various image restoration and enhancement methods have been developed by many researchers to improve the quality (visibility and highlight richer details) of underwater images. This paper introduces the overview of state-of-the-art underwater image restoration and enhancement techniques and classifies the approaches in two categories: image restoration (physical-based model) and image enhancement (nonphysical-based model). Furthermore, the classification of these two methods is elaborated. Then, the typical underwater image restoration and enhancement methods are discussed in detail, as well as a comprehensive study and fair evaluation of the methods is carried out from both qualitative and quantitative perspectives. Finally, the research process of underwater image restoration and enhancement is summarized and the suggestions for future research are prospected.

Robust and effective detection of a small target in an infrared maritime image is a key technology of maritime target search and tracking applications. Infrared small target detection is a challenging task due to the factors such as dim small targets and various complex backgrounds caused by sun glitters and strong waves. In this article, the integrated target saliency measure (ITSM) based on local and nonlocal spatial information is proposed to improve target detection performance. We combine local heterogeneity property of targets and nonlocal self-correlation property of background with targets' sparsity to separate real targets from background clutters. First, local heterogeneity calculation based on cross-window standard deviation (CSD) is proposed to extract candidate targets preliminarily, which enhances the local intensity difference between small targets and neighboring background. Meanwhile, low-rank representation (LRR) is applied to background prediction and removal, which is followed by adaptive threshold segmentation to enhance target saliency. Finally, we integrate the results obtained from the two steps mentioned above to further enhance targets and suppress background clutters. Then, real targets are extracted by an iterative threshold on the integrated map so as to generate the seed map, in addition, the target expansion strategy is exploited to keep full target areas. Experimental results on six datasets show that the proposed method outperforms the state-of-the-art methods in terms of robustness and target detection accuracy.

Images captured from underwater environment always suffer from color distortion, detail loss, and contrast reduction due to the medium scattering and absorption. This paper introduces an enhancement approach to improve the visual quality of underwater images, which does not require any dedicated devices or additional information more than the native single image. The proposed strategy consists of two steps: an improved white-balancing approach and an artificial multiple underexposure image fusion strategy for underwater imaging. In our white-balancing approach, the optimal color-compensated approach is determined by the sum of the Underwater Color Image Quality Evaluation (UCIQE) and the Underwater Image Quality Measure (UIQM). We get an optimal white-balanced version of the input by combining the well-known Gray World assumption and the optimal channel-compensated approach. In our artificial multiple underexposure image fusion strategy, first the gamma-correction operation is adopted to generate multiple underexposure versions. Then we propose to use ‘contrast’, ‘saturation’, and ‘well-exposedness’ as three weights, to be blended into the well-known multi-scale fusing scheme. Images enhanced by our strategy have a better visual quality than some state-of-the-art underwater dehazing techniques, through our validation with a wide range of qualitative and quantitative evaluations.

Infrared maritime target detection is the key technology of maritime target search systems. However, infrared images generally have the defects of low signal-to-noise ratio and low resolution. At the same time, the maritime environment is complicated and changeable. Under the interference of islands, waves and other disturbances, the brightness of small dim targets is easily obscured, which makes them difficult to distinguish. This is difficult for traditional target detection algorithms to deal with. In order to solve these problems, through the analysis of infrared maritime images under a variety of sea conditions including small dim targets, this paper concludes that in infrared maritime images, small targets occupy very few pixels, often do not have any edge contour information, and the gray value and contrast values are very low. The background such as island and strong sea wave occupies a large number of pixels, with obvious texture features, and often has a high gray value. By deeply analyzing the difference between the target and the background, this paper proposes a detection algorithm (SRGM) for infrared small dim targets under different maritime background. Firstly, this algorithm proposes an efficient maritime background filter for the common background in the infrared maritime image. Firstly, the median filter based on the sensitive region selection is used to extract the image background accurately, and then the background is eliminated by image difference with the original image. In addition, this article analyzes the differences in gradient features between strong interference caused by the background and targets, proposes a small dim target extraction operator with two analysis factors that fit the target features perfectly and combines the adaptive threshold segmentation to realize the accurate extraction of the small dim target. The experimental results show that compared with the current popular small dim target detection algorithms, this paper has better performance for target detection in various maritime environments.

Degradation of underwater images severely limits people to exploring and understanding underwater world, which has become a fundamental but vital issue needing to be addressed in underwater optics. In this paper, we develop an effective solution for underwater image enhancement. We first employ an adaptive-adjusted artificial multi-exposure fusion (A-AMEF) and a parameter adaptive-adjusted local color correction (PAL-CC) to generate a contrast-enhanced version and a color-corrected version from the input respectively. Then we put the contrast enhanced version into the famous guided filter to generate a smooth base-layer and a detail-information containing detail-layer. After that, we utilize the color channel transfer operation to transfer color information from the color-corrected version to the base-layer. Finally, the color-corrected base-layer and the detail-layer are added together simply to reconstruct the final enhanced output. Enhanced results obtained from the proposed solution performs better in visual quality, than those dehazed by some current techniques through our comprehensive validation both in quantitative and qualitative evaluations. In addition, this solution can be also utilized for dehazing fogged images or improving accuracy of other optical applications such as image segmentation and local feature points matching.