# RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment

arXiv: https://arxiv.org/abs/1712.05444 · HTML: https://mlsanity.com/paper/1712.05444/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2017.

## Built on by (most cited first, 13 of 13)

- [Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment](https://mlsanity.com/paper/2108.07948/) (2021)
- [DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild](https://mlsanity.com/paper/2405.19996/) (2024)
- [Assessing a Single Image in Reference-Guided Image Synthesis](https://mlsanity.com/paper/2112.04163/) (2021)
- [REQA: Coarse-to-fine Assessment of Image Quality to Alleviate the Range Effect](https://mlsanity.com/paper/2209.01760/) (2022)
- [Stochasticity-aware No-Reference Point Cloud Quality Assessment](https://mlsanity.com/paper/2401.08926/) (2024)
- [Bias-Aware Loss for Training Image and Speech Quality Prediction Models from Multiple Datasets](https://mlsanity.com/paper/2104.10217/) (2021)
- [Learning Transformer Features for Image Quality Assessment](https://mlsanity.com/paper/2112.00485/) (2021)
- [Geometric Generative Models based on Morphological Equivariant PDEs and GANs](https://mlsanity.com/paper/2403.14897/) (2024)
- [Residual-Guided Non-Intrusive Speech Quality Assessment](https://mlsanity.com/paper/2203.11499/) (2022)
- [K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment](https://mlsanity.com/paper/2307.04296/) (2023)
- [A Lightweight Parallel Framework for Blind Image Quality Assessment](https://mlsanity.com/paper/2402.12043/) (2024)
- [MetaQAP - A Meta-Learning Approach for Quality-Aware Pretraining in Image Quality Assessment](https://mlsanity.com/paper/2506.16601/) (2025)
- [A Reference-Free Framework for Evaluating Single-Frame ISP Pipelines](https://mlsanity.com/paper/2607.23321/) (2026)

## Abstract (arXiv)

Inspired by the free-energy brain theory, which implies that human visual system (HVS) tends to reduce uncertainty and restore perceptual details upon seeing a distorted image, we propose restorative adversarial net (RAN), a GAN-based model for no-reference image quality assessment (NR-IQA). RAN, which mimics the process of HVS, consists of three components: a restorator, a discriminator and an evaluator. The restorator restores and reconstructs input distorted image patches, while the discriminator distinguishes the reconstructed patches from the pristine distortion-free patches. After restoration, we observe that the perceptual distance between the restored and the distorted patches is monotonic with respect to the distortion level. We further define Gain of Restoration (GoR) based on this phenomenon. The evaluator predicts perceptual score by extracting feature representations from the distorted and restored patches to measure GoR. Eventually, the quality score of an input image is estimated by weighted sum of the patch scores. Experimental results on Waterloo Exploration, LIVE and TID2013 show the effectiveness and generalization ability of RAN compared to the state-of-the-art NR-IQA models.