Realistic Saliency Guided Image Enhancement
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.
648688baf95eb317dd4c7d8a
S. Mahdi H. Miangoleh
admin_assigned
2023-06-12T09:55:53.620Z
640009a7b09f82a81a240804
https://cdn-avatars.huggingface.co/v1/production/uploads/1677724011955-noauth.jpeg
S. Mahdi H. Miangoleh
user
mahdi-miangoleh
648688baf95eb317dd4c7d8b
Zoya Bylinskii
648688baf95eb317dd4c7d8c
Eric Kee
648688baf95eb317dd4c7d8d
Eli Shechtman
648688baf95eb317dd4c7d8e
Yağız Aksoy
Realistic Saliency Guided Image Enhancement
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.
A realism loss for saliency-guided image enhancement maintains photo realism while effectively attenuating distractions and amplifying subjects, outperforming other approaches with lower resource consumption.
realism loss saliency-guided image enhancement photo realism distractions objects of interest