Capturing images is a key part of automation for high-level tasks such as scene text recognition. Low-light conditions pose a challenge for high-level perception stacks, which are often optimized on well-lit, artifact-free images. Reconstruction methods for low-light images can produce well-lit counterparts, but typically at the cost of high-frequency details critical for downstream tasks. We propose Diffusion in the Dark (DiD), a diffusion model for low-light image reconstruction for text recognition. DiD provides qualitatively competitive reconstructions with that of state-of-the-art (SOTA), while preserving high-frequency details even in extremely noisy, dark conditions. We demonstrate that DiD, without any task-specific optimization, can outperform SOTA low-light methods in low-light text recognition on real images, bolstering the potential of diffusion models to solve ill-posed inverse problems.
@article{arxiv.2303.04291,
title = {Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition},
author = {Cindy M. Nguyen and Eric R. Chan and Alexander W. Bergman and Gordon Wetzstein},
journal= {arXiv preprint arXiv:2303.04291},
year = {2023}
}