Abstract
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.
Abstract (translated)
自动图像聚类是计算机视觉的基础，但将其应用于图像增强方面仍较为有限，主要原因是难以定义对这一特定任务有意义的聚类。为了解决这个问题，我们引入了CARDIE，这是一种无监督算法，根据图像的颜色和亮度内容进行聚类。此外，我们还提出了一种方法来量化图像增强算法对亮度分布和局部方差的影响。利用这种方法，我们展示了CARDIE产生的聚类比基于语义图像属性的聚类更相关于图像增强任务。进一步地，我们证明了CARDIE生成的聚类可以用来重新采样图像增强数据集，从而提高色调映射和去噪算法的表现。 为了鼓励采用并确保可重复性，我们在GitHub上公开发布了CARDIE代码。
URL
https://arxiv.org/abs/2509.06116
