# Wavelet-Enhanced Desnowing: A Novel Single Image Restoration Approach for Traffic Surveillance under Adverse Weather Conditions

arXiv: https://arxiv.org/abs/2503.01339 · HTML: https://mlsanity.com/paper/2503.01339/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 72 references that are in the map.
- First posted on arXiv 3 Mar 2025.

## Builds on (most cited first, 24 of 24)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Dynamic Convolution: Attention over Convolution Kernels](https://mlsanity.com/paper/1912.03458/) (2019)
- [Deep Joint Rain Detection and Removal from a Single Image](https://mlsanity.com/paper/1609.07769/) (2016)
- [DehazeNet: An End-to-End System for Single Image Haze Removal](https://mlsanity.com/paper/1601.07661/) (2016)
- [TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions](https://mlsanity.com/paper/2111.14813/) (2021)
- [DesnowNet: Context-Aware Deep Network for Snow Removal](https://mlsanity.com/paper/1708.04512/) (2017)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [A Survey on All-in-One Image Restoration: Taxonomy, Evaluation and Future Trends](https://mlsanity.com/paper/2410.15067/) (2024)
- [Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2405.04964/) (2024)
- [Prompt-In-Prompt Learning for Universal Image Restoration](https://mlsanity.com/paper/2312.05038/) (2023)
- [DyNet: Dynamic Convolution for Accelerating Convolutional Neural Networks](https://mlsanity.com/paper/2004.10694/) (2020)
- [MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing](https://mlsanity.com/paper/2207.05621/) (2022)
- [Revisiting Dynamic Convolution via Matrix Decomposition](https://mlsanity.com/paper/2103.08756/) (2021)
- [Textual Prompt Guided Image Restoration](https://mlsanity.com/paper/2312.06162/) (2023)
- [SDWNet: A Straight Dilated Network with Wavelet Transformation for Image Deblurring](https://mlsanity.com/paper/2110.05803/) (2021)
- [Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification](https://mlsanity.com/paper/1611.06345/) (2016)
- [Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restoration](https://mlsanity.com/paper/2407.19139/) (2024)
- [Gaussian Dynamic Convolution for Efficient Single-Image Segmentation](https://mlsanity.com/paper/2104.08783/) (2021)
- [LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images](https://mlsanity.com/paper/2209.02197/) (2022)
- [Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation](https://mlsanity.com/paper/2407.00676/) (2024)
- [Nordic Vehicle Dataset (NVD): Performance of vehicle detectors using newly captured NVD from UAV in different snowy weather conditions](https://mlsanity.com/paper/2304.14466/) (2023)
- [TANet: Triplet Attention Network for All-In-One Adverse Weather Image Restoration](https://mlsanity.com/paper/2410.08177/) (2024)
- [Efficient Convolutional Network Learning using Parametric Log based Dual-Tree Wavelet ScatterNet](https://mlsanity.com/paper/1708.09259/) (2017)
- [ConStyle v2: A Strong Prompter for All-in-One Image Restoration](https://mlsanity.com/paper/2406.18242/) (2024)

## Abstract (arXiv)

Image restoration under adverse weather conditions refers to the process of removing degradation caused by weather particles while improving visual quality. Most existing deweathering methods rely on increasing the network scale and data volume to achieve better performance which requires more expensive computing power. Also, many methods lack generalization for specific applications. In the traffic surveillance screener, the main challenges are snow removal and veil effect elimination. In this paper, we propose a wavelet-enhanced snow removal method that use a Dual-Tree Complex Wavelet Transform feature enhancement module and a dynamic convolution acceleration module to address snow degradation in surveillance images. We also use a residual learning restoration module to remove veil effects caused by rain, snow, and fog. The proposed architecture extracts and analyzes information from snow-covered regions, significantly improving snow removal performance. And the residual learning restoration module removes veiling effects in images, enhancing clarity and detail. Experiments show that it performs better than some popular desnowing methods. Our approach also demonstrates effectiveness and accuracy when applied to real traffic surveillance images.