title: Realistic Saliency Guided Image Enhancement
summary: 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.
paper_authors._id: 648688baf95eb317dd4c7d8a
paper_authors.name: S. Mahdi H. Miangoleh
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-12T09:55:53.620Z
paper_authors._id: 640009a7b09f82a81a240804
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1677724011955-noauth.jpeg
paper_authors.fullname: S. Mahdi H. Miangoleh
paper_authors.type: user
paper_authors.user: mahdi-miangoleh
paper_authors._id: 648688baf95eb317dd4c7d8b
paper_authors.name: Zoya Bylinskii
paper_authors._id: 648688baf95eb317dd4c7d8c
paper_authors.name: Eric Kee
paper_authors._id: 648688baf95eb317dd4c7d8d
paper_authors.name: Eli Shechtman
paper_authors._id: 648688baf95eb317dd4c7d8e
paper_authors.name: Yağız Aksoy
paper_title: Realistic Saliency Guided Image Enhancement
paper_summary: 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.
paper_ai_keywords: realism loss saliency-guided image enhancement distractors objects of interest