Geodesy

Quantum mechanics

Benchmark (surveying)

Engineering

Computer security

Systems engineering

Archaeology

Component (thermodynamics)

Computer science

Thermodynamics

Physics

Task (project management)

Geography

Image (mathematics)

Image fusion

Artificial intelligence

Underwater

Image enhancement

Term (time)

Perspective (graphical)

Computer vision

Adversarial system

Key (lock)

Signal Processing Image Communication

Underwater image enhancement algorithms have attracted much attention in underwater vision task. However, these algorithms are mainly evaluated on different datasets and metrics. In this paper, we utilize an effective and public underwater benchmark dataset including diverse underwater degradation scenes to enlarge the test scale and propose a fusion adversarial network for enhancing real underwater images. Meanwhile, the multiple inputs and well-designed multi-term adversarial loss can not only introduce multiple input image features, but also balance the impact of multi-term loss functions. The proposed network tested on the benchmark dataset achieves better or comparable performance than the other state-of-the-art methods in terms of qualitative and quantitative evaluations. Moreover, the ablation study experimentally validates the contributions of each component and hyper-parameter setting of loss functions.

DewaterNet: A fusion adversarial real underwater image enhancement network