基于色彩亮度聚类，提升图像增强任务的分组相关性
CARDIE: clustering algorithm on relevant descriptors for image enhancement
- 依据颜色与亮度内容无监督聚类图像
- 聚类结果比语义属性更利于增强算法优化
- 可重采样数据集，提升调色与去噪性能
自动图像聚类是计算机视觉的核心技术，但在图像增强中的应用受限，主要源于难以定义对任务有意义的聚类。为此，我们提出CARDIE，一种基于图像色彩与亮度内容的无监督聚类算法。同时，提出一种量化图像增强算法对亮度分布和局部方差影响的方法。实验表明，CARDIE生成的聚类比基于语义属性的聚类更契合图像增强任务。此外，利用这些聚类可对图像增强数据集进行重采样，显著提升色调映射与去噪算法的性能。为促进应用与复现，我们已将CARDIE代码公开于GitHub。
原文摘要 · Abstract (English)
Automatic image clustering is a cornerstone of computer vision, yet its application to image enhancement remains limited, primarily due to the difficulty of defining clusters that are meaningful for this specific task. To address this issue, we introduce CARDIE, an unsupervised algorithm that clusters images based on their color and luminosity content. In addition, we introduce a method to quantify the impact of image enhancement algorithms on luminance distribution and local variance. Using this method, we demonstrate that CARDIE produces clusters more relevant to image enhancement than those derived from semantic image attributes. Furthermore, we demonstrate that CARDIE clusters can be leveraged to resample image enhancement datasets, leading to improved performance for tone mapping and denoising algorithms. To encourage adoption and ensure reproducibility, we publicly release CARDIE code on our GitHub.
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