Construction of Maritime Image Deblurring Dataset Based on Blur Components Extraction Model

This paper introduces a Blur Components Extraction Model(BCEM) and presents a synthetic image deblurring dataset specialized for maritime environments, Maritime Blur Dataset(MBD). The proposed BCEM extracts blur kernels from unaligned pairs of sharp and blurred images captured with a single camera, without requiring additional hardware or motion sensors. Using the extracted blur kernels, MBD is constructed by convolving them with high-resolution sharp images of maritime scenes that include ships, buoys, and ocean waves-elements rarely considered in terrestrial benchmark datasets. The proposed MBD is used to train deep learning-based image deblurring models, and their performance is evaluated through both qualitative and quantitative comparisons. By efficiently isolating motion blur components such as engine-induced vibrations, the proposed approach allows for the construction of high-quality and realistic deblurring datasets.