# PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment

arXiv: https://arxiv.org/abs/2412.15527 · HTML: https://mlsanity.com/paper/2412.15527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 20 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild](https://mlsanity.com/paper/2005.13983/) (2020)
- [Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration](https://mlsanity.com/paper/2311.16845/) (2023)
- [dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs](https://mlsanity.com/paper/1904.06505/) (2019)
- [Perceptual Quality Prediction on Authentically Distorted Images Using a Bag of Features Approach](https://mlsanity.com/paper/1609.04757/) (2016)
- [Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank](https://mlsanity.com/paper/2303.09101/) (2023)
- [Data-Efficient Image Quality Assessment with Attention-Panel Decoder](https://mlsanity.com/paper/2304.04952/) (2023)
- [Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment](https://mlsanity.com/paper/2108.07948/) (2021)
- [SyreaNet: A Physically Guided Underwater Image Enhancement Framework Integrating Synthetic and Real Images](https://mlsanity.com/paper/2302.08269/) (2023)
- [Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data](https://mlsanity.com/paper/1904.08632/) (2019)
- [Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image](https://mlsanity.com/paper/1909.11983/) (2019)
- [Test Time Adaptation for Blind Image Quality Assessment](https://mlsanity.com/paper/2307.14735/) (2023)
- [UIF: An Objective Quality Assessment for Underwater Image Enhancement](https://mlsanity.com/paper/2205.09392/) (2022)
- [Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement](https://mlsanity.com/paper/2102.00670/) (2021)
- [UID2021: An Underwater Image Dataset for Evaluation of No-reference Quality Assessment Metrics](https://mlsanity.com/paper/2204.08813/) (2022)

## Abstract (arXiv)

In this paper, we propose a Physical Imaging Guided perceptual framework for Underwater Image Quality Assessment (UIQA), termed PIGUIQA. First, we formulate UIQA as a comprehensive problem that considers the combined effects of direct transmission attenuation and backward scattering on image perception. By leveraging underwater radiative transfer theory, we systematically integrate physics-based imaging estimations to establish quantitative metrics for these distortions. Second, recognizing spatial variations in image content significance and human perceptual sensitivity to distortions, we design a module built upon a neighborhood attention mechanism for local perception of images. This module effectively captures subtle features in images, thereby enhancing the adaptive perception of distortions on the basis of local information. Third, by employing a global perceptual aggregator that further integrates holistic image scene with underwater distortion information, the proposed model accurately predicts image quality scores. Extensive experiments across multiple benchmarks demonstrate that PIGUIQA achieves state-of-the-art performance while maintaining robust cross-dataset generalizability. The implementation is publicly available at https://github.com/WeizhiXian/PIGUIQA