# Realistic Saliency Guided Image Enhancement

arXiv: https://arxiv.org/abs/2306.06092 · HTML: https://mlsanity.com/paper/2306.06092/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 30 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Semantic Understanding of Scenes through the ADE20K Dataset](https://mlsanity.com/paper/1608.05442/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [The relativistic discriminator: a key element missing from standard GAN](https://mlsanity.com/paper/1807.00734/) (2018)
- [SalGAN: Visual Saliency Prediction with Generative Adversarial Networks](https://mlsanity.com/paper/1701.01081/) (2017)
- [Learning a Discriminative Model for the Perception of Realism in Composite Images](https://mlsanity.com/paper/1510.00477/) (2015)
- [EML-NET:An Expandable Multi-Layer NETwork for Saliency Prediction](https://mlsanity.com/paper/1805.01047/) (2018)
- [Deep Saliency Prior for Reducing Visual Distraction](https://mlsanity.com/paper/2109.01980/) (2021)
- [Look here! A parametric learning based approach to redirect visual attention](https://mlsanity.com/paper/2008.05413/) (2020)
- [Saliency Driven Image Manipulation](https://mlsanity.com/paper/1612.02184/) (2016)
- [Guiding human gaze with convolutional neural networks](https://mlsanity.com/paper/1712.06492/) (2017)

## Built on by (most cited first, 14 of 14)

- [DeCo: Decoupled Human-Centered Diffusion Video Editing with Motion Consistency](https://mlsanity.com/paper/2408.07481/) (2024)
- [Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping](https://mlsanity.com/paper/2510.09741/) (2025)
- [Rapid Salient Object Detection with Difference Convolutional Neural Networks](https://mlsanity.com/paper/2507.01182/) (2025)
- [NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2604.14816/) (2026)
- [Trends, Applications, and Challenges in Human Attention Modelling](https://mlsanity.com/paper/2402.18673/) (2024)
- [AIM 2024 Challenge on Video Saliency Prediction: Methods and Results](https://mlsanity.com/paper/2409.14827/) (2024)
- [Data Augmentation via Latent Diffusion for Saliency Prediction](https://mlsanity.com/paper/2409.07307/) (2024)
- [Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency](https://mlsanity.com/paper/2406.18588/) (2024)
- [Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images](https://mlsanity.com/paper/2506.21925/) (2025)
- [Multilevel Saliency-Guided Self-Supervised Learning for Image Anomaly Detection](https://mlsanity.com/paper/2311.18332/) (2023)
- [Exploring Saliency Bias in Manipulation Detection](https://mlsanity.com/paper/2402.07338/) (2024)
- [Revisiting Salient Object Detection from an Observer-Centric Perspective](https://mlsanity.com/paper/2602.06369/) (2026)
- [DGSSM: Diffusion guided state-space models for multimodal salient object detection](https://mlsanity.com/paper/2604.17585/) (2026)
- [Dotting the Eye: An Intent-Driven Image Retouching Agent for Visual Focus Enhancement](https://mlsanity.com/paper/2609.01148/) (2026)

## Abstract (arXiv)

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.