Related papers: Personalized Generative Low-light Image Denoising and Enhancement

Personalized Generative Low-light Image Denoising and Enhancement

URL: http://arxiv.org/abs/2412.14327v1
Date: Wed, 18 Dec 2024 20:43:38 GMT
Title: Personalized Generative Low-light Image Denoising and Enhancement
Authors: Xijun Wang, Prateek Chennuri, Yu Yuan, Bole Ma, Xingguang Zhang, Stanley Chan,
Abstract summary: We propose Personalized Generative Denoising (PGD) by building a diffusion model customized for different users. Our core innovation is an identity-consistent physical buffer that extracts the physical attributes of the person from the gallery. Over a wide range of low-light testing scenarios, we show that PGD achieves superior image denoising and enhancement performance.
Score: 3.2423254294855735
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Abstract: While smartphone cameras today can produce astonishingly good photos, their performance in low light is still not completely satisfactory because of the fundamental limits in photon shot noise and sensor read noise. Generative image restoration methods have demonstrated promising results compared to traditional methods, but they suffer from hallucinatory content generation when the signal-to-noise ratio (SNR) is low. Recognizing the availability of personalized photo galleries on users' smartphones, we propose Personalized Generative Denoising (PGD) by building a diffusion model customized for different users. Our core innovation is an identity-consistent physical buffer that extracts the physical attributes of the person from the gallery. This ID-consistent physical buffer provides a strong prior that can be integrated with the diffusion model to restore the degraded images, without the need of fine-tuning. Over a wide range of low-light testing scenarios, we show that PGD achieves superior image denoising and enhancement performance compared to existing diffusion-based denoising approaches.

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