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

Abstract

Images are indispensable for the automation of highlevel tasks, such as text recognition. Low-light conditions pose a challenge for these 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 highfrequency details critical for downstream tasks. We propose Diffusion in the Dark (DiD), a diffusion model for lowlight image reconstruction that provides qualitatively competitive reconstructions with that of 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 for ill-posed inverse problems.