Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition
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Images are indispensable for the automation of high-level 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 high-frequency details critical for downstream tasks. We propose Diffusion in the Dark (DiD), a diffusion model for low-light 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.
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