Underwater image enhancement is critical for a variety of marine applications such as exploration, navigation, and biological research. However, underwater images often suffer from quality degradation due to factors such as light absorption, scattering, and color distortion. Although current deep learning methods have achieved better performance, it is difficult to balance the enhancement performance and computational efficiency in practical applications, and some methods tend to cause performance degradation on high-resolution large-size input images. To alleviate the above points, this paper proposes an efficient network GFRENet for underwater image enhancement utilizing gated linear units (GLUs) and fast Fourier convolution (FFC). GLUs help to selectively retain the most relevant features, thus improving the overall enhancement performance. FFC enables efficient and robust frequency domain processing to effectively address the unique challenges posed by the underwater environment. Extensive experiments on benchmark datasets show that our approach significantly outperforms existing state-of-the-art techniques in both qualitative and quantitative metrics. The proposed network provides a promising solution for real-time underwater image enhancement, making it suitable for practical deployment in various underwater applications.

Dehazing individual remote sensing (RS) images is an effective approach to enhance the quality of hazy remote sensing imagery. However, current dehazing methods exhibit substantial systemic and computational complexity. Such complexity not only hampers the straightforward analysis and comparison of these methods but also undermines their practical effectiveness on actual data, attributed to the overtraining and overfitting of model parameters. To mitigate these issues, we introduce a novel dehazing network for non-uniformly hazy RS images: GLUENet, designed for both lightweightness and computational efficiency. Our approach commences with the implementation of the classical U-Net, integrated with both local and global residuals, establishing a robust base for the extraction of multi-scale information. Subsequently, we construct basic convolutional blocks using gated linear units and efficient channel attention, incorporating depth-separable convolutional layers to efficiently aggregate spatial information and transform features. Additionally, we introduce a fusion block based on efficient channel attention, facilitating the fusion of information from different stages in both encoding and decoding to enhance the recovery of texture details. GLUENet’s efficacy was evaluated using both synthetic and real remote sensing dehazing datasets, providing a comprehensive assessment of its performance. The experimental results demonstrate that GLUENet’s performance is on par with state-of-the-art (SOTA) methods and surpasses the SOTA methods on our proposed real remote sensing dataset. Our method on the real remote sensing dehazing dataset has an improvement of 0.31 dB for the PSNR metric and 0.13 for the SSIM metric, and the number of parameters and computations of the model are much lower than the optimal method.