Abstract
DNN-based methods have been successful in Image Signal Processor (ISP) and image enhancement (IE) tasks. However, the cost of creating training data for these tasks is considerably higher than for other tasks, making it difficult to prepare large-scale datasets. Also, creating personalized ISP and IE with minimal training data can lead to new value streams since preferred image quality varies depending on the person and use case. While semi-supervised learning could be a potential solution in such cases, it has rarely been utilized for these tasks. In this paper, we realize semi-supervised learning for ISP and IE leveraging a RAW image reconstruction (sRGB-to-RAW) method. Although existing sRGB-to-RAW methods can generate pseudo-RAW image datasets that improve the accuracy of RAW-based high-level computer vision tasks such as object detection, their quality is not sufficient for ISP and IE tasks that require precise image quality definition. Therefore, we also propose a sRGB-to-RAW method that can improve the image quality of these tasks. The proposed semi-supervised learning with the proposed sRGB-to-RAW method successfully improves the image quality of various models on various datasets.
Abstract (translated)
基于深度神经网络（DNN）的方法在图像信号处理（ISP）和图像增强（IE）任务中取得了成功。然而，为这些任务创建训练数据的成本远高于其他任务，这使得准备大规模的数据集变得困难。此外，在使用少量训练数据的情况下创造个性化的ISP和IE可以产生新的价值流，因为不同的个人和用例对理想图像质量的需求不同。尽管半监督学习在这种情况下可能是一个潜在的解决方案，但其在这些任务中的应用却很少见。在这篇论文中，我们通过利用RAW图像重建（sRGB到RAW）方法实现了ISP和IE领域的半监督学习。虽然现有的sRGB到RAW的方法可以生成伪RAW图像数据集以提高基于RAW的高级计算机视觉任务（如目标检测）的准确性，但这些数据的质量不足以满足需要精确图像质量定义的ISP和IE任务的需求。因此，我们还提出了一种改进这些任务中图像质量的sRGB到RAW方法。结合提出的半监督学习和sRGB到RAW的方法，在各种数据集上对多种模型的图像质量得到了显著提升。
URL
https://arxiv.org/abs/2504.02345
