# Deep Edge-Aware Interactive Colorization against Color-Bleeding Effects

arXiv: https://arxiv.org/abs/2107.01619 · HTML: https://mlsanity.com/paper/2107.01619/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 32 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Colorful Image Colorization](https://mlsanity.com/paper/1603.08511/) (2016)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [Deep multi-scale video prediction beyond mean square error](https://mlsanity.com/paper/1511.05440/) (2015)
- [Learning Representations for Automatic Colorization](https://mlsanity.com/paper/1603.06668/) (2016)
- [Real-Time User-Guided Image Colorization with Learned Deep Priors](https://mlsanity.com/paper/1705.02999/) (2017)
- [Deep Exemplar-based Colorization](https://mlsanity.com/paper/1807.06587/) (2018)
- [Instance-aware Image Colorization](https://mlsanity.com/paper/2005.10825/) (2020)
- [Deep Exemplar-based Video Colorization](https://mlsanity.com/paper/1906.09909/) (2019)
- [ChromaGAN: Adversarial Picture Colorization with Semantic Class Distribution](https://mlsanity.com/paper/1907.09837/) (2019)
- [Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence](https://mlsanity.com/paper/2005.05207/) (2020)
- [Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks](https://mlsanity.com/paper/1906.11888/) (2019)
- [Pixelated Semantic Colorization](https://mlsanity.com/paper/1901.10889/) (2019)
- [Side Window Filtering](https://mlsanity.com/paper/1905.07177/) (2019)
- [Pixel-level Semantics Guided Image Colorization](https://mlsanity.com/paper/1808.01597/) (2018)
- [Interactive Deep Colorization With Simultaneous Global and Local Inputs](https://mlsanity.com/paper/1801.09083/) (2018)
- [Semantic-driven Colorization](https://mlsanity.com/paper/2006.07587/) (2020)

## Built on by (most cited first, 12 of 12)

- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Low-Light Image Enhancement via Structure Modeling and Guidance](https://mlsanity.com/paper/2305.05839/) (2023)
- [GLARE: Low Light Image Enhancement via Generative Latent Feature based Codebook Retrieval](https://mlsanity.com/paper/2407.12431/) (2024)
- [PalGAN: Image Colorization with Palette Generative Adversarial Networks](https://mlsanity.com/paper/2210.11204/) (2022)
- [BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-based Video Colorization](https://mlsanity.com/paper/2212.02268/) (2022)
- [Semantic-Sparse Colorization Network for Deep Exemplar-based Colorization](https://mlsanity.com/paper/2112.01335/) (2021)
- [Language-based Photo Color Adjustment for Graphic Designs](https://mlsanity.com/paper/2308.03059/) (2023)
- [Improving Sketch Colorization using Adversarial Segmentation Consistency](https://mlsanity.com/paper/2301.08590/) (2023)
- [SPColor: Semantic Prior Guided Exemplar-based Image Colorization](https://mlsanity.com/paper/2304.06255/) (2023)
- [Human vs Objective Evaluation of Colourisation Performance](https://mlsanity.com/paper/2204.05200/) (2022)
- [Computer-aided Colorization State-of-the-science: A Survey](https://mlsanity.com/paper/2410.02288/) (2024)
- [Enabling Region-Specific Control via Lassos in Point-Based Colorization](https://mlsanity.com/paper/2412.13469/) (2024)

## Abstract (arXiv)

Deep neural networks for automatic image colorization often suffer from the color-bleeding artifact, a problematic color spreading near the boundaries between adjacent objects. Such color-bleeding artifacts debase the reality of generated outputs, limiting the applicability of colorization models in practice. Although previous approaches have attempted to address this problem in an automatic manner, they tend to work only in limited cases where a high contrast of gray-scale values are given in an input image. Alternatively, leveraging user interactions would be a promising approach for solving this color-breeding artifacts. In this paper, we propose a novel edge-enhancing network for the regions of interest via simple user scribbles indicating where to enhance. In addition, our method requires a minimal amount of effort from users for their satisfactory enhancement. Experimental results demonstrate that our interactive edge-enhancing approach effectively improves the color-bleeding artifacts compared to the existing baselines across various datasets.