Autonomous driving algorithms usually employ sRGB images as model input due to their compatibility with the human visual system. However, visually pleasing sRGB images are possibly sub-optimal for downstream tasks when compared to RAW images. The availability of RAW images is constrained by the difficulties in collecting real-world driving data and the associated challenges of annotation. To address this limitation and support research in RAW-domain driving perception, we design a novel and ultra-lightweight RAW reconstruction method. The proposed model introduces a learnable color correction matrix (CCM), which uses only a single convolutional layer to approximate the complex inverse image signal processor (ISP). Experimental results demonstrate that simulated RAW (simRAW) images generated by our method provide performance improvements equivalent to those produced by more complex inverse ISP methods when pretraining RAW-domain object detectors, which highlights the effectiveness and practicality of our approach.

翻译：自动驾驶算法通常采用sRGB图像作为模型输入，这源于其与人类视觉系统的兼容性。然而，与RAW图像相比，视觉上令人愉悦的sRGB图像对于下游任务可能并非最优选择。RAW图像的可用性受到现实驾驶数据采集困难及相关标注挑战的限制。为突破这一局限并支持RAW域驾驶感知研究，我们设计了一种新颖且超轻量级的RAW重建方法。该模型引入了一个可学习的色彩校正矩阵（CCM），仅使用单个卷积层来近似复杂的反向图像信号处理器（ISP）。实验结果表明，在预训练RAW域目标检测器时，通过本方法生成的模拟RAW（simRAW）图像所提供的性能提升，与更复杂的反向ISP方法所产生的效果相当，这凸显了我们方法的有效性和实用性。