# Subjective and Objective De-raining Quality Assessment Towards Authentic Rain Image

arXiv: https://arxiv.org/abs/1909.11983 · HTML: https://mlsanity.com/paper/1909.11983/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2019.

## Built on by (most cited first, 18 of 18)

- [From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain Removal](https://mlsanity.com/paper/2308.03867/) (2023)
- [Continual Learning for Blind Image Quality Assessment](https://mlsanity.com/paper/2102.09717/) (2021)
- [Domain Adaptation for Underwater Image Enhancement](https://mlsanity.com/paper/2108.09650/) (2021)
- [No-Reference Image Quality Assessment by Hallucinating Pristine Features](https://mlsanity.com/paper/2108.04165/) (2021)
- [Dual Degradation Representation for Joint Deraining and Low-Light Enhancement in the Dark](https://mlsanity.com/paper/2305.03997/) (2023)
- [Multi-scale Attentive Image De-raining Networks via Neural Architecture Search](https://mlsanity.com/paper/2207.00728/) (2022)
- [Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity](https://mlsanity.com/paper/2211.00837/) (2022)
- [A survey on IQA](https://mlsanity.com/paper/2109.00347/) (2021)
- [Language bias in Visual Question Answering: A Survey and Taxonomy](https://mlsanity.com/paper/2111.08531/) (2021)
- [Unveiling the Underwater World: CLIP Perception Model-Guided Underwater Image Enhancement](https://mlsanity.com/paper/2507.06234/) (2025)
- [A Review of 3D Face Reconstruction From a Single Image](https://mlsanity.com/paper/2110.09299/) (2021)
- [Blind Image Quality Assessment: A Brief Survey](https://mlsanity.com/paper/2312.16551/) (2023)
- [SemiDDM-Weather: A Semi-supervised Learning Framework for All-in-one Adverse Weather Removal](https://mlsanity.com/paper/2409.19679/) (2024)
- [A Review of Deep Learning Based Image Super-resolution Techniques](https://mlsanity.com/paper/2201.10521/) (2022)
- [Deep Decomposition and Bilinear Pooling Network for Blind Night-Time Image Quality Evaluation](https://mlsanity.com/paper/2205.05880/) (2022)
- [Perceptual Constancy Constrained Single Opinion Score Calibration for Image Quality Assessment](https://mlsanity.com/paper/2404.19595/) (2024)
- [PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment](https://mlsanity.com/paper/2412.15527/) (2024)
- [Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions](https://mlsanity.com/paper/2605.13158/) (2026)

## Abstract (arXiv)

Images acquired by outdoor vision systems easily suffer poor visibility and annoying interference due to the rainy weather, which brings great challenge for accurately understanding and describing the visual contents. Recent researches have devoted great efforts on the task of rain removal for improving the image visibility. However, there is very few exploration about the quality assessment of de-rained image, even it is crucial for accurately measuring the performance of various de-raining algorithms. In this paper, we first create a de-raining quality assessment (DQA) database that collects 206 authentic rain images and their de-rained versions produced by 6 representative single image rain removal algorithms. Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images. To quantitatively measure the quality of de-rained image with non-uniform artifacts, we propose a bi-directional feature embedding network (B-FEN) which integrates the features of global perception and local difference together. Experiments confirm that the proposed method significantly outperforms many existing universal blind image quality assessment models. To help the research towards perceptually preferred de-raining algorithm, we will publicly release our DQA database and B-FEN source code on https://github.com/wqb-uestc.