Dark-ISP: Enhancing RAW Image Processing for Low-Light Object Detection

• URL: http://arxiv.org/abs/2509.09183v1
• Date: Thu, 11 Sep 2025 06:44:43 GMT
• Title: Dark-ISP: Enhancing RAW Image Processing for Low-Light Object Detection
• Authors: Jiasheng Guo, Xin Gao, Yuxiang Yan, Guanghao Li, Jian Pu,
• Abstract summary: Low-light Object detection is crucial for many real-world applications but remains challenging due to degraded image quality.<n>We propose a lightweight and self-adaptive Image Signal Processing (ISP) plugin, Dark-ISP, which directly processes Bayer RAW images in dark environments.<n>Our method outperforms state-of-the-art RGB- and RAW-based detection approaches, achieving superior results with minimal parameters in challenging low-light environments.
• Score: 22.292648672901066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-light Object detection is crucial for many real-world applications but remains challenging due to degraded image quality. While recent studies have shown that RAW images offer superior potential over RGB images, existing approaches either use RAW-RGB images with information loss or employ complex frameworks. To address these, we propose a lightweight and self-adaptive Image Signal Processing (ISP) plugin, Dark-ISP, which directly processes Bayer RAW images in dark environments, enabling seamless end-to-end training for object detection. Our key innovations are: (1) We deconstruct conventional ISP pipelines into sequential linear (sensor calibration) and nonlinear (tone mapping) sub-modules, recasting them as differentiable components optimized through task-driven losses. Each module is equipped with content-aware adaptability and physics-informed priors, enabling automatic RAW-to-RGB conversion aligned with detection objectives. (2) By exploiting the ISP pipeline's intrinsic cascade structure, we devise a Self-Boost mechanism that facilitates cooperation between sub-modules. Through extensive experiments on three RAW image datasets, we demonstrate that our method outperforms state-of-the-art RGB- and RAW-based detection approaches, achieving superior results with minimal parameters in challenging low-light environments.

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