Enzu Peng (彭恩祖)

Research Interest

• Deep Learning
• Machine Learning
• Computer Vision

Research Experience

As a Research Assistant at the Deep-Sea Resource Exploration and Development Laboratory, I contributed to the restoration of turbid underwater images of cobalt crusts using homomorphic filtering and polarization imaging. Additionally, I was involved in setting up simulations for deep-sea mining, performing flow field simulations using Fluent, and drafting a manuscript that is currently under review as the first author. I also led a project on an integrated chive harvesting and bundling machine, securing funding and obtaining a utility model patent. I am currently ranked fourth in my class.

Research Project Achievements:

Restoration of Turbid Underwater Images of Cobalt Crusts Using Combined Homomorphic Filtering and Polarization Imaging System

Enzu Peng 1, Chengyi Liu 1, and Haiming Zhao 1,2

Abstract: Marine cobalt-rich crusts, extensively used in industries such as aerospace, automotive, and electronics, are crucial mineral resources located on the ocean floor. To effectively exploit these valuable resources, underwater imaging is essential for real-time detection and distribution mapping in mining areas. However, the presence of suspended particles in the seabed mining environment severely degrades image quality due to light scattering and absorption, hindering the effective identification of the target objects. Traditional image processing techniques—including spatial and frequency domain methods—are ineffective in addressing the interference caused by suspended particles and offer only limited enhancement effects. This paper proposes a novel underwater image restoration method that combines polarization imaging and homomorphic filtering. By exploiting the differences in polarization characteristics between suspended particles and target objects, polarization imaging is used to separate backscattered light from the target signal, enhancing the clarity of the cobalt crust images. Homomorphic filtering is then applied to improve the intensity distribution and contrast of the orthogonal polarization images. To optimize the parameters, a genetic algorithm is used with image quality evaluation indices as the fitness function. The proposed method was compared with traditional image processing techniques and classical polarization imaging methods. Experimental results demonstrate that the proposed approach more effectively suppresses backscattered light, enhancing the clarity of target object features. With significant improvements in image quality confirmed by several no-reference quality metrics, the method shows promise as a solution for high-quality underwater imaging in turbid environments, particularly for deep-sea mining of cobalt-rich crusts.