# Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model

arXiv: https://arxiv.org/abs/2503.18512 · HTML: https://mlsanity.com/paper/2503.18512/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 39 of its 50 references that are in the map.
- First posted on arXiv 24 Mar 2025.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 30 of 39)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021)
- [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [Designing a Practical Degradation Model for Deep Blind Image Super-Resolution](https://mlsanity.com/paper/2103.14006/) (2021)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [Exploiting Diffusion Prior for Real-World Image Super-Resolution](https://mlsanity.com/paper/2305.07015/) (2023)
- [Activating More Pixels in Image Super-Resolution Transformer](https://mlsanity.com/paper/2205.04437/) (2022)
- [SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models](https://mlsanity.com/paper/2104.14951/) (2021)
- [Toward Real-World Single Image Super-Resolution: A New Benchmark and A New Model](https://mlsanity.com/paper/1904.00523/) (2019)

## Built on by (most cited first, 29 of 29)

- [DiT-IC: Aligned Diffusion Transformer for Efficient Image Compression](https://mlsanity.com/paper/2603.13162/) (2026)
- [Inference-time Scaling for Diffusion-based Audio Super-resolution](https://mlsanity.com/paper/2508.02391/) (2025)
- [Texture Vector-Quantization and Reconstruction Aware Prediction for Generative Super-Resolution](https://mlsanity.com/paper/2509.23774/) (2025)
- [Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model](https://mlsanity.com/paper/2510.23816/) (2025)
- [Bridging Fidelity-Reality with Controllable One-Step Diffusion for Image Super-Resolution](https://mlsanity.com/paper/2512.14061/) (2025)
- [FinPercep-RM: A Fine-grained Reward Model and Co-evolutionary Curriculum for RL-based Real-world Super-Resolution](https://mlsanity.com/paper/2512.22647/) (2025)
- [Joint Geometric and Trajectory Consistency Learning for One-Step Real-World Super-Resolution](https://mlsanity.com/paper/2602.24240/) (2026)
- [ATD: Improved Transformer with Adaptive Token Dictionary for Image Restoration](https://mlsanity.com/paper/2603.02581/) (2026)
- [FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution](https://mlsanity.com/paper/2605.03749/) (2026)
- [OP4KSR: One-Step Patch-Free 4K Super-Resolution with Periodic Artifact Suppression](https://mlsanity.com/paper/2605.13457/) (2026)
- [Motion is the Choreographer: Learning Latent Pose Dynamics for Seamless Sign Language Generation](https://mlsanity.com/paper/2508.04049/) (2025)
- [GEWDiff: Geometric Enhanced Wavelet-based Diffusion Model for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2511.07103/) (2025)
- [Unifying Heterogeneous Degradations: Uncertainty-Aware Diffusion Bridge Model for All-in-One Image Restoration](https://mlsanity.com/paper/2601.21592/) (2026)
- [Bird-SR: Bidirectional Reward-Guided Diffusion for Real-World Image Super-Resolution](https://mlsanity.com/paper/2602.07069/) (2026)
- [Breaking Watermarks in the Frequency Domain: A Modulated Diffusion Attack Framework](https://mlsanity.com/paper/2604.22220/) (2026)
- [Bridging Restoration and Generation in One-step Diffusion for Real-World Image Super-Resolution](https://mlsanity.com/paper/2604.24136/) (2026)
- [Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration](https://mlsanity.com/paper/2605.00310/) (2026)
- [RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network](https://mlsanity.com/paper/2506.21905/) (2025)
- [U-DAVI: Uncertainty-Aware Diffusion-Prior-Based Amortized Variational Inference for Image Reconstruction](https://mlsanity.com/paper/2602.11704/) (2026)
- [AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution](https://mlsanity.com/paper/2603.00589/) (2026)
- [QUSR: Quality-Aware and Uncertainty-Guided Image Super-Resolution Diffusion Model](https://mlsanity.com/paper/2603.09125/) (2026)
- [SpiralDiff: Spiral Diffusion with LoRA for RGB-to-RAW Conversion Across Cameras](https://mlsanity.com/paper/2603.14885/) (2026)
- [AlloSR2: Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows](https://mlsanity.com/paper/2604.19238/) (2026)
- [S3-Diff: Structural Semantic Synergy Diffusion Model for High Fidelity Super Resolution of Pathological Images](https://mlsanity.com/paper/2608.03540/) (2026)
- [Morphology-Aware Implicit Super-Resolution Network for Pathological Images](https://mlsanity.com/paper/2608.03664/) (2026)
- [Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution](https://mlsanity.com/paper/2608.25998/) (2026)
- [ProSR: Semantic-Prototype-Guided Discrete Modeling for Physically Consistent SAR Super-Resolution](https://mlsanity.com/paper/2609.02377/) (2026)
- [Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation](https://mlsanity.com/paper/2609.27131/) (2026)
- [PredRA: Fast Medical Image Translation by Deterministic Component Extraction and Controlled Stochastic Refinement](https://mlsanity.com/paper/2609.31912/) (2026)

## Abstract (arXiv)

Diffusion-based image super-resolution methods have demonstrated significant advantages over GAN-based approaches, particularly in terms of perceptual quality. Building upon a lengthy Markov chain, diffusion-based methods possess remarkable modeling capacity, enabling them to achieve outstanding performance in real-world scenarios. Unlike previous methods that focus on modifying the noise schedule or sampling process to enhance performance, our approach emphasizes the improved utilization of LR information. We find that different regions of the LR image can be viewed as corresponding to different timesteps in a diffusion process, where flat areas are closer to the target HR distribution but edge and texture regions are farther away. In these flat areas, applying a slight noise is more advantageous for the reconstruction. We associate this characteristic with uncertainty and propose to apply uncertainty estimate to guide region-specific noise level control, a technique we refer to as Uncertainty-guided Noise Weighting. Pixels with lower uncertainty (i.e., flat regions) receive reduced noise to preserve more LR information, therefore improving performance. Furthermore, we modify the network architecture of previous methods to develop our Uncertainty-guided Perturbation Super-Resolution (UPSR) model. Extensive experimental results demonstrate that, despite reduced model size and training overhead, the proposed UWSR method outperforms current state-of-the-art methods across various datasets, both quantitatively and qualitatively.