Realistic Saliency Guided Image Enhancement
This provides a viable solution for automating image enhancement and photo cleanup operations, though it is incremental as it builds on existing saliency-guided methods.
The paper tackled the problem of automating professional photo cleanup by de-emphasizing distractors and enhancing subjects while maintaining realism, achieving higher realism and effectiveness compared to recent approaches with a smaller memory footprint and runtime.
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.