According to maritime image histograms’ statistic and analysis, the histogram of pure maritime image obeys Gaussian distribution approximately, thus Three Adaptive Sub-histograms Equalization (TASHE) algorithm for maritime image enhancement is proposed in this paper. First, the characteristics of pure maritime image’s histogram are studied, then the adaptive threshold’s optimal selection strategy for the histogram’s division is discussed, finally the implement of three sub-histograms is described. This paper employs visible gray maritime image, visible color maritime image and infrared maritime image to verify the enhancement algorithm’s effectiveness and robustness, the experimental results show that TASHE algorithm can not only keep the maritime image’s mean brightness and naturalness, but also improve the maritime image’s contrast without the noise and artifacts. The objective image quality assessment also indicates that TASHE algorithm can improve the original maritime image’s Enhancement Measure by Entropy (EME) value, furthermore, when a maritime image is pre-processed by TASHE algorithm, the maritime target’s Detection Rate (DR) can be improved.

Images captured under foggy or hazy weather conditions are affected by the scattering of atmospheric particles, resulting in decreased contrast and color variation, thereby limiting their practical applications. In recent years, deep learning methods showcase significant advancements in image dehazing. However, the complexity and degradation factors in hazy images challenge the generalization capacity of dehazing methods. This paper comprehensively reviews the recent developments in single-image dehazing techniques based on deep learning. From the perspectives of Convolutional Neural Networks (CNN) and Generative Adversarial Networks (GAN), different models are introduced and classified into four categories: Encoder-Decoder, Multi-Module, Multi-Branch, and Dual-Generative Adversarial Networks. The robustness and effectiveness of deep learning models are analyzed by comparing their performance and model complexity on public datasets. Additionally, limitations of current benchmark datasets and evaluation metrics are identified, and unresolved issues and future research directions are discussed. Our efforts in this paper will serve as a comprehensive reference for future research and call for further development in deep learning-based image dehazing.

Aiming at maritime infrared target detection with low contrast influenced by maritime clutter and illumination, this paper proposes a Modified Histogram Equalization with Edge Fusion (MHEEF) pre-processing algorithm in backlight maritime scenes and establishes Local-Contrast Saliency Models with Double Scale and Modes (LCMDSM) for detecting a target with the properties of positive and negative contrast. We propose a local-contrast saliency mathematical model with double modes in the extension of only one mode. Then, the big scale and small scale are combined into one Target Detection Unit (TDU), which can approach the “from bottom to up” mechanism of the Visual Attention Model (VAM) better and identify the target with a suitable size, approaching the target’s actual shape. In the experimental results and analysis, clutter, foggy, backlight, and dim maritime scenes are chosen to verify the effectiveness of the target detection algorithm. From the enhancement result, the LCMDSM algorithm can achieve a Detection Rate (DR) with a value of 98.26% under each maritime scene on the average level and can be used in real-time detection with low computational cost.