A Novel Approach to Maritime Image Dehazing Based on a Large Kernel Encoder–Decoder Network with Multihead Pyramids

Abstract

With the continuous increase in human–robot integration, battlefield formation is experiencing a revolutionary change. Unmanned aerial vehicles, unmanned surface vessels, combat robots, and other new intelligent weapons and equipment will play an essential role on future battlefields by performing various tasks, including situational reconnaissance, monitoring, attack, and communication relay. Real-time monitoring of maritime scenes is the basis of battle-situation and threat estimation in naval battlegrounds. However, images of maritime scenes are usually accompanied by haze, clouds, and other disturbances, which blur the images and diminish the validity of their contents. This will have a severe adverse impact on many downstream tasks. A novel large kernel encoder–decoder network with multihead pyramids (LKEDN-MHP) is proposed to address some maritime image dehazing-related issues. The LKEDN-MHP adopts a multihead pyramid approach to form a hybrid representation space comprising reflection, shading, and semanteme. Unlike standard convolutional neural networks (CNNs), the LKEDN-MHP uses many kernels with a 7 × 7 or larger scale to extract features. To reduce the computational burden, depthwise (DW) convolution combined with re-parameterization is adopted to form a hybrid model stacked by a large number of different receptive fields, further enhancing the hybrid receptive fields. To restore the natural hazy maritime scenes as much as possible, we apply digital twin technology to build a simulation system in virtual space. The final experimental results based on the evaluation metrics of the peak signal-to-noise ratio, structural similarity index measure, Jaccard index, and Dice coefficient show that our LKEDN-MHP significantly enhances dehazing and real-time performance compared with those of state-of-the-art approaches based on vision transformers (ViTs) and generative adversarial networks (GANs).

Learning degradation-aware visual prompt for maritime image restoration under adverse weather conditions

“…This method utilizes a haze removal algorithm to eliminate the haze component from the glow-free layer and employs illumination compensation to restore natural illumination in the glow layer. Yang et al (Yang et al, 2022) proposed a multi-head pyramid large kernel encoder-decoder network (LKEDN-MHP) for denoising tasks in maritime images. This method utilizes the transmission map extracted from the guidance image as an additional input to improve the network performance.…”

Improved Test Input Prioritization Using Verification Monitors with False Prediction Cluster Centroids

“…Deep learning (DL) has been demonstrating cutting-edge performance in various applications [1,2]. Despite the great performance of the DL models, they are prone to errors caused by different factors, including data bias, architectural limitations, and training cost constraints [3,4].…”