Summary
The method uses a complementary cascaded network with both raindrop→streak and streak→raindrop branches, fuses their outputs with attention, searches deraining blocks with neural architecture search, and introduces the real-world RainDS dataset.
Research question
Methods designed for only rain streaks or only lens raindrops do not cover mixed real-world rain, while paired real/rain-free data for multiple degradation types is scarce.
What the paper contributes
- Introduces a complementary cascaded network that performs the two removal orders in parallel.
- Fuses complementary branch results through an attention-based module and searches deraining architectures automatically.
- Introduces RainDS with streak-only, raindrop-only, and combined rain images plus rain-free ground truth.
Evidence and evaluation scope
Experiments on existing benchmarks and RainDS report performance above the compared state of the art. Exact metrics and train/test conditions should be cited from the CVPR paper.
Scope and limitations
The framework and RainDS address the rain types represented in the search space and dataset. Generalization to other weather effects, sensors, or unpaired deployment domains is a separate question.
Positioning for related work
This is not simply a one-pass joint restoration network: its key design is complementary cascades in both degradation-removal orders, attention fusion, architecture search, and a new real-world dataset.
Related-work context
Quan et al. jointly address raindrops and rain streaks with a complementary cascaded network that fuses both removal orders, uses neural architecture search for deraining blocks, and introduces the RainDS dataset.
A concise, neutral description of how this paper can be situated in related work.
Official abstract
Existing rain-removal algorithms often tackle either rain streak removal or raindrop removal, and thus may fail to handle real-world rainy scenes. Besides, the lack of real-world deraining datasets comprising different types of rain and their corresponding rain-free ground-truth also impedes deraining algorithm development. In this paper, we aim to address real-world deraining problems from two aspects. First, we propose a complementary cascaded network architecture, namely CCN, to remove rain streaks and raindrops in a unified framework. Specifically, our CCN removes raindrops and rain streaks in a complementary fashion, i.e., raindrop removal followed by rain streak removal and vice versa, and then fuses the results via an attention based fusion module. Considering significant shape and structure differences between rain streaks and raindrops, it is difficult to manually design a sophisticated network to remove them effectively. Thus, we employ neural architecture search to adaptively find optimal architectures within our specified deraining search space. Second, we present a new real-world rain dataset, namely RainDS, to prosper the development of deraining algorithms in practical scenarios. RainDS consists of rain images in different types and their corresponding rain-free ground-truth, including rain streak only, raindrop only, and both of them. Extensive experimental results on both existing benchmarks and RainDS demonstrate that our method outperforms the state-of-the-art.
The abstract is reproduced for scholarly identification and remains under the paper publisher/authors’ original copyright; it is not covered by this page’s CC BY license.
Evidence references
| What to verify | Location in the paper |
|---|---|
| Problem statement | Abstract |
| Method and contributions | Abstract; complementary cascaded network, attention fusion, and NAS sections |
| Evaluation statement | Abstract; existing benchmarks and RainDS experiments |
Primary source: IEEE version of record (CVPR 2021 version of record; CVF open-access copy has alternate pagination 9147-9156).
How to cite
Cite the paper—not this explainer—for scientific claims. Cite this page only when reusing its original commentary.
Ruijie Quan, Xin Yu, Yuanzhi Liang, and Yi Yang. “Removing Raindrops and Rain Streaks in One Go.” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021), 9143-9152. https://doi.org/10.1109/CVPR46437.2021.00903.
Reuse policy
Original explanatory text on this page is licensed under CC BY 4.0 with attribution and a link to this page. Paper title, abstract, figures, and bibliographic metadata are excluded and retain their original rights. Creative Commons Attribution 4.0 International.