# PI-REC: Progressive Image Reconstruction Network With Edge and Color Domain

arXiv: https://arxiv.org/abs/1903.10146 · HTML: https://mlsanity.com/paper/1903.10146/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 4 of 4)

- [Conditional Frechet Inception Distance](https://mlsanity.com/paper/2103.11521/) (2021)
- [Facial-Sketch Synthesis: A New Challenge](https://mlsanity.com/paper/2112.15439/) (2021)
- [Re:Draw -- Context Aware Translation as a Controllable Method for Artistic Production](https://mlsanity.com/paper/2401.03499/) (2024)
- [Flexible Portrait Image Editing with Fine-Grained Control](https://mlsanity.com/paper/2204.01318/) (2022)

## Abstract (arXiv)

We propose a universal image reconstruction method to represent detailed images purely from binary sparse edge and flat color domain. Inspired by the procedures of painting, our framework, based on generative adversarial network, consists of three phases: Imitation Phase aims at initializing networks, followed by Generating Phase to reconstruct preliminary images. Moreover, Refinement Phase is utilized to fine-tune preliminary images into final outputs with details. This framework allows our model generating abundant high frequency details from sparse input information. We also explore the defects of disentangling style latent space implicitly from images, and demonstrate that explicit color domain in our model performs better on controllability and interpretability. In our experiments, we achieve outstanding results on reconstructing realistic images and translating hand drawn drafts into satisfactory paintings. Besides, within the domain of edge-to-image translation, our model PI-REC outperforms existing state-of-the-art methods on evaluations of realism and accuracy, both quantitatively and qualitatively.