用预训练扩散模型重用相机ISP流程，提升暗光原始图像质量。
DarkDiff: Advancing Low-Light Raw Enhancement by Retasking Diffusion Models for Camera ISP
- 重用预训练扩散模型，结合相机ISP流程进行暗光原始图像增强
- 在三个挑战性数据集上超越当前最优，显著改善图像细节与色彩还原
- 适合需要高保真暗光成像的摄影、安防及移动端设备开发者
极端低光照条件下的高质量摄影对数字相机具有重要意义。随着计算硬件的发展，传统相机图像信号处理器（ISP）算法正逐渐被高效深度网络替代，以更智能地增强噪声原始图像。然而，现有基于回归的模型常最小化像素误差，导致暗光照片过度平滑或深阴影区域细节丢失。近期研究尝试从头训练扩散模型以缓解此问题，但依然难以恢复清晰图像细节与准确色彩。本文提出一种新框架，通过将预训练生成式扩散模型重新适配至相机ISP流程，实现低光原始图像增强。大量实验表明，该方法在三个具有挑战性的低光原始图像基准测试中，均在感知质量上优于现有最先进水平。
原文摘要 · Abstract (English)
High-quality photography in extreme low-light conditions is challenging but impactful for digital cameras. With advanced computing hardware, traditional camera image signal processor (ISP) algorithms are gradually being replaced by efficient deep networks that enhance noisy raw images more intelligently. However, existing regression-based models often minimize pixel errors and result in oversmoothing of low-light photos or deep shadows. Recent work has attempted to address this limitation by training a diffusion model from scratch, yet those models still struggle to recover sharp image details and accurate colors. We introduce a novel framework to enhance low-light raw images by retasking pre-trained generative diffusion models with the camera ISP. Extensive experiments demonstrate that our method outperforms the state-of-the-art in perceptual quality across three challenging low-light raw image benchmarks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。