Modern cameras typically offer two types of image states: a minimally
processed linear raw RGB image representing the raw sensor data, and a
highly-processed non-linear image state, such as the sRGB state. The CIE-XYZ
color space is a device-independent linear space used as part of the camera
pipeline and can be helpful for computer vision tasks, such as image
deblurring, dehazing, and color recognition tasks in medical applications,
where color accuracy is important. However, images are usually saved in
non-linear states, and achieving CIE-XYZ color images using conventional
methods is not always possible. To tackle this issue, classical methodologies
have been developed that focus on reversing the acquisition pipeline. More
recently, supervised learning has been employed, using paired CIE-XYZ and sRGB
representations of identical images. However, obtaining a large-scale dataset
of CIE-XYZ and sRGB pairs can be challenging. To overcome this limitation and
mitigate the reliance on large amounts of paired data, self-supervised learning
(SSL) can be utilized as a substitute for relying solely on paired data. This
paper proposes a framework for using SSL methods alongside paired data to
reconstruct CIE-XYZ images and re-render sRGB images, outperforming existing
approaches. The proposed framework is applied to the sRGB2XYZ dataset.

此研究论文提出了一个使用自监督学习方法和成对数据结合的框架，用于重建 CIE-XYZ 图像和重新生成 sRGB 图像，优于现有方法。该框架应用于 sRGB2XYZ 数据集。