Underwater image processing is an intelligence research field that has great potential to help developers better explore the underwater environment. Underwater image processing has been used in a wide variety of fields, such as underwater microscopic detection, terrain scanning, mine detection, telecommunication cables, and autonomous underwater vehicles. However, underwater imagery suffers from strong absorption, scattering, color distortion, and noise from the artificial light sources, causing image blur, haziness, and a bluish or greenish tone. Therefore, the enhancement of underwater imagery can be divided into two methods: 1) underwater image dehazing and 2) underwater image color restoration. This paper presents the reason for underwater image degradation, surveys the state-of-the-art intelligence algorithms like deep learning methods in underwater image dehazing and restoration, demonstrates the performance of underwater image dehazing and color restoration with different methods, introduces an underwater image color evaluation metric, and provides an overview of the major underwater image applications. Finally, we summarize the application of underwater image processing.

In industrial applications, thermal infrared images, which are commonly used, often suffer from issues such as low contrast and blurred details. Traditional image enhancement algorithms are limited in their effectiveness in improving the visual quality of thermal infrared images due to the specific nature of the application. Therefore, we propose a dual Convolutional Neural Network (CNN) combined with an attention mechanism to address the challenges of enhancing low-quality thermal infrared images and improving their visual quality. Firstly, we employ two parallel sub-networks to extract both global and local features. In one sub-network, we utilize a sparse mechanism incorporating dilated convolutions, while the other sub-network employs Feature Attention (FA) blocks based on channel attention and pixel attention. This architecture significantly enhances the feature extraction capability. The use of attention mechanisms allows the network to filter out irrelevant background information, enabling more flexible feature extraction. Finally, through a simple yet effective fusion block, we thoroughly integrate the extracted features to achieve an optimal fusion strategy, ensuring the highest quality enhancement of the final image. Extensive experiments on benchmark datasets and real images demonstrate that our proposed method outperforms other state-of-the-art models in terms of objective evaluation metrics and subjective assessments. The generated images also exhibit superior visual quality.

The most direct way to find the electrical switchgear fault is to use infrared thermal imaging technology for temperature measurement. However, infrared thermal imaging images are usually polluted by noise, and there are problems such as low contrast and blurred edges. To solve these problems, this article proposes a dual convolutional neural network model based on nonsubsampled contourlet transform (NSCT). First, the overall structure of the model is made wider by combining the two networks. Compared with the deeper convolutional neural network, the dual convolutional neural network (CNN) improves the denoising performance without increasing the computational cost too much. Secondly, the model uses NSCT and inverse NSCT to obtain more texture information and avoid the gridding effect. It achieves a good balance between noise reduction performance and detail retention. A large number of simulation experiments show that the model has the ability to deal with synthetic noise and real noise, which has high practical value.