Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition
- URL: http://arxiv.org/abs/2303.04291v2
- Date: Tue, 31 Oct 2023 00:18:09 GMT
- Title: Diffusion in the Dark: A Diffusion Model for Low-Light Text Recognition
- Authors: Cindy M. Nguyen, Eric R. Chan, Alexander W. Bergman, Gordon Wetzstein
- Abstract summary: Diffusion in the Dark (DiD) is a diffusion model for low-light image reconstruction for text recognition.
We demonstrate that DiD, without any task-specific optimization, can outperform SOTA low-light methods in low-light text recognition on real images.
- Score: 78.50328335703914
- License: http://creativecommons.org/licenses/by/4.0/
- 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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