基于真实显著性引导的图像增强

摘要

专业摄影师常进行的常见编辑操作包括清理操作：减弱分散注意力的元素并增强主体。这些编辑是具有挑战性的，需要在操纵观众注意力的同时保持照片逼真度之间取得微妙平衡。尽管最近的方法可以自豪地展示成功的注意力减弱或增强示例，但大多数方法也常常出现不真实的编辑。我们提出了一种适用于基于显著性的图像增强的逼真损失，以在各种图像类型中保持高逼真度，同时减弱干扰因素并增强感兴趣的对象。与专业摄影师的评估证实，我们实现了逼真性和有效性的双重目标，并在他们自己的数据集上胜过最近的方法，同时需要更小的内存占用和运行时间。因此，我们提供了一种可行的解决方案，用于自动化图像增强和照片清理操作。

English

Common editing operations performed by professional photographers include the cleanup operations: de-emphasizing distracting elements and enhancing subjects. These edits are challenging, requiring a delicate balance between manipulating the viewer's attention while maintaining photo realism. While recent approaches can boast successful examples of attention attenuation or amplification, most of them also suffer from frequent unrealistic edits. We propose a realism loss for saliency-guided image enhancement to maintain high realism across varying image types, while attenuating distractors and amplifying objects of interest. Evaluations with professional photographers confirm that we achieve the dual objective of realism and effectiveness, and outperform the recent approaches on their own datasets, while requiring a smaller memory footprint and runtime. We thus offer a viable solution for automating image enhancement and photo cleanup operations.

基于真实显著性引导的图像增强

Realistic Saliency Guided Image Enhancement

摘要

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