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arXiv · 2303.04291

Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition

Cindy M. Nguyen·Eric R. Chan·Alexander W. Bergman·Gordon Wetzstein
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Abstract

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.

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Cindy M. Nguyen, Eric R. Chan, Alexander W. Bergman, Gordon Wetzstein. 2023-10-31. Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition. https://arxiv.org/abs/2303.04291

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