Super-Resolution Reconstruction of Weak Targets on Water Surfaces: A Generative Adversarial Network Approach Based on Implicit Neural Representation

• Published in: Traitement du signal 2023-12, Vol.40 (6), p.2701-2710
• Main Authors: Bi, Qilin, Lin, Zhiqiang, Chen, Boren, Lai, Minlin, Guo, Yanyao, Lv, Youjie, Tang, Yali, Huang, Chuxin
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Datasets; Deep learning; Generative adversarial networks; Image enhancement; Image processing; Image reconstruction; Image resolution; Neural networks; Representations; Weather
• ISSN: 0765-0019; 1958-5608
• DOI: 10.18280/ts.400630

In the realm of super-resolution reconstruction, challenges are posed by the interference of various weather conditions, such as rain and fog, as well as complex environmental backgrounds, notably water surfaces. This research addresses the critical issue of feature information loss, lack of edge detail, and inconsistent lighting in the reconstruction of weak targets on water surfaces. The study introduces a novel approach employing a generative adversarial network (GAN) based on implicit neural representation. This method is specifically tailored for enhancing the clarity and detail of small targets on water surfaces. The methodology involves constructing a super-resolution (SR) image generation model that leverages the implicit neural representation of images. This model adeptly handles the nuances of small water surface targets. A comprehensive evaluation framework is developed, incorporating network weights, deviation coefficients, edge loss, and balance loss as key indicators. This aids in the formulation of an adaptive loss function for the SR image generation model, significantly improving the model's performance in challenging conditions. To validate the efficacy of the proposed approach, datasets of low-resolution (LR) and high-resolution (HR) images of weak targets on water surfaces were compiled. These datasets were created using simultaneous imaging of the target with both HR and LR cameras. Comparative analysis with existing popular algorithms demonstrates the superiority of the proposed method in SR reconstruction of weak targets in complex water surface environments. The results high-light the model’s enhanced ability to identify and classify weak targets with high reliability and accuracy, even under challenging weather and environmental conditions.