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.

翻译：专业摄影师常用的编辑操作包括清理操作：弱化干扰元素并突出主体。这些编辑具有挑战性，需要在操控观众注意力与保持照片真实感之间取得微妙平衡。尽管近期方法在注意力衰减或增强方面取得了成功案例，但大多数方法仍存在频繁的非真实编辑问题。我们提出了一种用于显著性引导图像增强的真实感损失函数，以在各类图像中保持高真实感，同时弱化干扰物并增强感兴趣目标。专业摄影师的评估证实，我们实现了真实感与有效性的双重目标，在自有数据集上优于近期方法，且所需内存占用与运行时间更小。因此，我们为图像增强与照片清理操作的自动化提供了一种可行方案。