Local-CycleGAN: a general end-to-end network for visual enhancement in complex deep-water environment
- PDF / 2,487,871 Bytes
- 12 Pages / 595.276 x 790.866 pts Page_size
- 83 Downloads / 133 Views
Local-CycleGAN: a general end-to-end network for visual enhancement in complex deep-water environment Xianhui Zong 1 & Zhehan Chen 1 & Dadong Wang 2 Accepted: 5 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Underwater image analysis is crucial for many applications such as seafloor survey, biological and environment monitoring, underwater vehicle navigation, inspection and maintenance of underwater infrastructure etc. However, due to light absorption and scattering, the images acquired underwater are always blurry and distorted in color. Most existing image enhancement algorithms typically focus on a few features of the imaging environments, and enhanced results depend on the characteristics of original images. In this study, a local cycle-consistent generative adversarial network is proposed to enhance images acquired in a complex deep-water environment. The proposed network uses a combination of a local discriminator and a global discriminator. Additionally, quality-monitor loss is adopted to evaluate the effect of the generated images. Experimental results show that the local cycle-consistent generative adversarial network is robust and can be generalized for many different image enhancement tasks in different types of complex deep-water environment with varied turbidity. Keywords Local auxiliary discriminator . Quality-monitor loss function . Cycle-consistency generative adversarial network . Image enhancement . Turbid deep-water environment
1 Introduction With the development of robotic technology, remotely operated underwater vehicles (ROVs), autonomous underwater vehicles (AUVs), and other underwater equipment are increasingly widely used in various deep-water engineering projects. Optical visual systems are one of the main components of underwater intelligent equipment and are important for object recognition and navigation in a complex deep-water environment. However, the underwater optical image processing is a challenging field and associated with complexity and uncertainty. Underwater images exhibit low brightness, blurs, and color distortions, these attributes significantly affect the visual effect. The attenuations of blue and green lights are weaker than that of red light in the underwater transmission process, and thus the underwater images generally appear green, yellow, cyan, and blue in color.
* Zhehan Chen [email protected] 1
School of Mechanical Engineering, University of Science & Technology Beijing, Beijing, China
2
Quantitative Imaging Research Team, Data61, CSIRO, Sydney, Australia
Water bodies are affected by the diameter and quantity of suspended particles and exhibit different turbidity levels. At present, the dark channel prior (DCP) algorithm [1], the Retinex algorithm [2], the Automatic Color Enhancement (ACE) algorithm [3], and other traditional image enhancement algorithms are mostly used to solve the underwater image enhancement problem. The DCP algorithm restores a blurred image by estimating the atmospheric scattering model,
Data Loading...
