Large-Scale Empirical Image Enhancement Studies with Diverse HPC systems

High-performance computing (HPC) is vital for advancing AI research in computer vision, where training on high-resolution datasets requires significant computational power. Using the NSF-funded Accelerating Computing for Emerging Sciences (ACES) testbed at Texas A&M University, we leveraged Graphcore IPUs, NVIDIA H100 GPUs, and A30 GPUs to conduct a large-scale empirical study of 170 model configurations spanning CNN-, GAN-, Transformer-, and Diffusion-based architectures. This enabled us to address an open question: Is underwater image enhancement (UIE) truly beneficial for underwater object detection? We trained five object detectors across 17 enhancement domains and two datasets. Our results show that most UIE methods degrade detection accuracy, while select diffusion-based approaches that preserve key features can mitigate this drop. HPC resources also allowed us to compare GPU and IPU performance. These findings guide the practical use of UIE in marine vision and highlight the importance of equitable HPC access for large-scale AI research.