Traditional image detail enhancement is local filter-based or global filter-based. In both approaches, the original image is first divided into the base layer and the detail layer, and then the enhanced image is obtained by amplifying the detail layer. Our method is different, and its innovation lies in the special way to get the image detail layer. The detail layer in our method is obtained by updating the residual features, and the updating mechanism is usually based on searching and matching similar patches. However, due to the diversity of image texture features, perfect matching is often not possible. In this paper, the process of searching and matching is treated as a thermodynamic process, where the Metropolis theorem can minimize the internal energy and get the global optimal solution of this task, that is, to find a more suitable feature for a better detail enhancement performance. Extensive experiments have proven that our algorithm can achieve better results in quantitative metrics testing and visual effects evaluation. The source code can be obtained from the link.

翻译：传统的图像细节增强方法基于局部滤波或全局滤波。在这两种方法中，首先将原始图像分解为基底层和细节层，然后通过放大细节层获得增强图像。我们的方法有所不同，其创新之处在于获取图像细节层的特殊方式。本文方法通过更新残差特征来获得细节层，而该更新机制通常基于相似块的搜索与匹配。然而，由于图像纹理特征的多样性，往往无法实现完美匹配。本文将搜索与匹配的过程视为热力学过程，其中Metropolis定理能够最小化系统内能并获取该任务的全局最优解，即为实现更好的细节增强性能找到更合适的特征。大量实验证明，本算法在定量指标测试和视觉效果评估中均能取得更优结果。源代码可通过链接获取。