# Deep Dense Multi-scale Network for Snow Removal Using Semantic and Geometric Priors

arXiv: https://arxiv.org/abs/2103.11298 · HTML: https://mlsanity.com/paper/2103.11298/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 69 references that are in the map.
- First posted on arXiv 21 Mar 2021.

## Builds on (most cited first, 27 of 27)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Deep Multi-scale Convolutional Neural Network for Dynamic Scene Deblurring](https://mlsanity.com/paper/1612.02177/) (2016)
- [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)
- [Density-aware Single Image De-raining using a Multi-stream Dense Network](https://mlsanity.com/paper/1802.07412/) (2018)
- [Enforcing geometric constraints of virtual normal for depth prediction](https://mlsanity.com/paper/1907.12209/) (2019)
- [Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining](https://mlsanity.com/paper/1807.05698/) (2018)
- [Attentive Generative Adversarial Network for Raindrop Removal from a Single Image](https://mlsanity.com/paper/1711.10098/) (2017)
- [DesnowNet: Context-Aware Deep Network for Snow Removal](https://mlsanity.com/paper/1708.04512/) (2017)
- [Image De-raining Using a Conditional Generative Adversarial Network](https://mlsanity.com/paper/1701.05957/) (2017)
- [Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset](https://mlsanity.com/paper/1904.01538/) (2019)
- [Deblurring by Realistic Blurring](https://mlsanity.com/paper/2004.01860/) (2020)
- [Hierarchical Multi-Scale Attention for Semantic Segmentation](https://mlsanity.com/paper/2005.10821/) (2020)
- [Heavy Rain Image Restoration: Integrating Physics Model and Conditional Adversarial Learning](https://mlsanity.com/paper/1904.05050/) (2019)
- [Densely Connected Pyramid Dehazing Network](https://mlsanity.com/paper/1803.08396/) (2018)
- [Clearing the Skies: A deep network architecture for single-image rain removal](https://mlsanity.com/paper/1609.02087/) (2016)
- [Single Image Deraining: A Comprehensive Benchmark Analysis](https://mlsanity.com/paper/1903.08558/) (2019)
- [Adversarial Spatio-Temporal Learning for Video Deblurring](https://mlsanity.com/paper/1804.00533/) (2018)
- [Residual Conv-Deconv Grid Network for Semantic Segmentation](https://mlsanity.com/paper/1707.07958/) (2017)
- [ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring](https://mlsanity.com/paper/2103.04260/) (2021)
- [Dual Attention-in-Attention Model for Joint Rain Streak and Raindrop Removal](https://mlsanity.com/paper/2103.07051/) (2021)
- [Semantic Single-Image Dehazing](https://mlsanity.com/paper/1804.05624/) (2018)

## Built on by (most cited first, 30 of 82)

- [TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions](https://mlsanity.com/paper/2111.14813/) (2021)
- [Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models](https://mlsanity.com/paper/2207.14626/) (2022)
- [OneRestore: A Universal Restoration Framework for Composite Degradation](https://mlsanity.com/paper/2407.04621/) (2024)
- [MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing](https://mlsanity.com/paper/2308.14036/) (2023)
- [Restoring Images in Adverse Weather Conditions via Histogram Transformer](https://mlsanity.com/paper/2407.10172/) (2024)
- [GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions](https://mlsanity.com/paper/2305.17863/) (2023)
- [Diff-Plugin: Revitalizing Details for Diffusion-based Low-level Tasks](https://mlsanity.com/paper/2403.00644/) (2024)
- [MSP-Former: Multi-Scale Projection Transformer for Single Image Desnowing](https://mlsanity.com/paper/2207.05621/) (2022)
- [MB-TaylorFormer V2: Improved Multi-branch Linear Transformer Expanded by Taylor Formula for Image Restoration](https://mlsanity.com/paper/2501.04486/) (2025)
- [UMC: A Unified Bandwidth-efficient and Multi-resolution based Collaborative Perception Framework](https://mlsanity.com/paper/2303.12400/) (2023)
- [Visual-Instructed Degradation Diffusion for All-in-One Image Restoration](https://mlsanity.com/paper/2506.16960/) (2025)
- [Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2403.01497/) (2024)
- [Teaching Tailored to Talent: Adverse Weather Restoration via Prompt Pool and Depth-Anything Constraint](https://mlsanity.com/paper/2409.15739/) (2024)
- [MWFormer: Multi-Weather Image Restoration Using Degradation-Aware Transformers](https://mlsanity.com/paper/2411.17226/) (2024)
- [Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation](https://mlsanity.com/paper/2309.13700/) (2023)
- [Low-light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion](https://mlsanity.com/paper/2401.03788/) (2024)
- [AWRaCLe: All-Weather Image Restoration using Visual In-Context Learning](https://mlsanity.com/paper/2409.00263/) (2024)
- [LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration](https://mlsanity.com/paper/2410.15385/) (2024)
- [RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration](https://mlsanity.com/paper/2505.18047/) (2025)
- [Counting Crowds in Bad Weather](https://mlsanity.com/paper/2306.01209/) (2023)
- [PromptRR: Diffusion Models as Prompt Generators for Single Image Reflection Removal](https://mlsanity.com/paper/2402.02374/) (2024)
- [Sharing Key Semantics in Transformer Makes Efficient Image Restoration](https://mlsanity.com/paper/2405.20008/) (2024)
- [Snow Mask Guided Adaptive Residual Network for Image Snow Removal](https://mlsanity.com/paper/2207.04754/) (2022)
- [SnowFormer: Context Interaction Transformer with Scale-awareness for Single Image Desnowing](https://mlsanity.com/paper/2208.09703/) (2022)
- [All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model](https://mlsanity.com/paper/2411.07445/) (2024)
- [GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration](https://mlsanity.com/paper/2411.17687/) (2024)
- [Exploring the Application of Large-scale Pre-trained Models on Adverse Weather Removal](https://mlsanity.com/paper/2306.09008/) (2023)
- [Always Clear Days: Degradation Type and Severity Aware All-In-One Adverse Weather Removal](https://mlsanity.com/paper/2310.18293/) (2023)
- [Genuine Knowledge from Practice: Diffusion Test-Time Adaptation for Video Adverse Weather Removal](https://mlsanity.com/paper/2403.07684/) (2024)
- [Multi-weather Cross-view Geo-localization Using Denoising Diffusion Models](https://mlsanity.com/paper/2408.02408/) (2024)

## Abstract (arXiv)

Images captured in snowy days suffer from noticeable degradation of scene visibility, which degenerates the performance of current vision-based intelligent systems. Removing snow from images thus is an important topic in computer vision. In this paper, we propose a Deep Dense Multi-Scale Network (DDMSNet) for snow removal by exploiting semantic and geometric priors. As images captured in outdoor often share similar scenes and their visibility varies with depth from camera, such semantic and geometric information provides a strong prior for snowy image restoration. We incorporate the semantic and geometric maps as input and learn the semantic-aware and geometry-aware representation to remove snow. In particular, we first create a coarse network to remove snow from the input images. Then, the coarsely desnowed images are fed into another network to obtain the semantic and geometric labels. Finally, we design a DDMSNet to learn semantic-aware and geometry-aware representation via a self-attention mechanism to produce the final clean images. Experiments evaluated on public synthetic and real-world snowy images verify the superiority of the proposed method, offering better results both quantitatively and qualitatively.