# DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

arXiv: https://arxiv.org/abs/2004.04433 · HTML: https://mlsanity.com/paper/2004.04433/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 17 of 17)

- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [VariTex: Variational Neural Face Textures](https://mlsanity.com/paper/2104.05988/) (2021)
- [Super-NeRF: View-consistent Detail Generation for NeRF super-resolution](https://mlsanity.com/paper/2304.13518/) (2023)
- [Variational AutoEncoder for Reference based Image Super-Resolution](https://mlsanity.com/paper/2106.04090/) (2021)
- [TextIR: A Simple Framework for Text-based Editable Image Restoration](https://mlsanity.com/paper/2302.14736/) (2023)
- [Toward Interactive Modulation for Photo-Realistic Image Restoration](https://mlsanity.com/paper/2105.03085/) (2021)
- [Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration](https://mlsanity.com/paper/2203.08444/) (2022)
- [From Posterior Sampling to Meaningful Diversity in Image Restoration](https://mlsanity.com/paper/2310.16047/) (2023)
- [Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior](https://mlsanity.com/paper/2405.07044/) (2024)
- [Text-guided Explorable Image Super-resolution](https://mlsanity.com/paper/2403.01124/) (2024)
- [D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution](https://mlsanity.com/paper/2103.14373/) (2021)
- [Posterior Sampling for Image Restoration using Explicit Patch Priors](https://mlsanity.com/paper/2104.09895/) (2021)
- [VSpSR: Explorable Super-Resolution via Variational Sparse Representation](https://mlsanity.com/paper/2104.08575/) (2021)
- [Dream, Lift, Animate: From Single Images to Animatable Gaussian Avatars](https://mlsanity.com/paper/2507.15979/) (2025)
- [Rethinking Super-Resolution as Text-Guided Details Generation](https://mlsanity.com/paper/2207.06604/) (2022)
- [CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution](https://mlsanity.com/paper/2412.11609/) (2024)
- [A Generative Model for Hallucinating Diverse Versions of Super Resolution Images](https://mlsanity.com/paper/2102.06624/) (2021)

## Abstract (arXiv)

Super-resolution (SR) is by definition ill-posed. There are infinitely many plausible high-resolution variants for a given low-resolution natural image. Most of the current literature aims at a single deterministic solution of either high reconstruction fidelity or photo-realistic perceptual quality. In this work, we propose an explorative facial super-resolution framework, DeepSEE, for Deep disentangled Semantic Explorative Extreme super-resolution. To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution. In particular, it provides control of the semantic regions, their disentangled appearance and it allows a broad range of image manipulations. We validate DeepSEE on faces, for up to 32x magnification and exploration of the space of super-resolution. Our code and models are available at: https://mcbuehler.github.io/DeepSEE/