Underwater Image Enhancement for Depth Estimation via Various Image Processing Techniques

Abstract

Depth estimation is crucial for autonomous under-water vehicles (AUVs) to navigate effectively and understand their surroundings. While deep neural networks offer promising solutions for depth estimation, their efficacy in challenging underwater environments-characterized by low light conditions and noise-is limited. To address this challenge, this paper proposes a method to enhance underwater image quality, thereby bolstering the accuracy of depth estimation. The approach involves pre-processing underwater images using color compensation and light balancing techniques, alongside contrast and sharpness enhancements. Subsequently, depth estimation is performed utilizing the Udepth model on the enhanced images. The efficacy and accuracy of the proposed method are evaluated and presented, demonstrating its potential to enhance depth image quality for underwater robotics applications.