# Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining

arXiv: https://arxiv.org/abs/2404.01547 · HTML: https://mlsanity.com/paper/2404.01547/

## Key facts

- Cited by 54 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 65 references that are in the map.
- First posted on arXiv 2 Apr 2024.

## Builds on (most cited first, 30 of 47)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Uformer: A General U-Shaped Transformer for Image Restoration](https://mlsanity.com/paper/2106.03106/) (2021)
- [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021)
- [CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification](https://mlsanity.com/paper/2103.14899/) (2021)
- [Learning Continuous Image Representation with Local Implicit Image Function](https://mlsanity.com/paper/2012.09161/) (2020)
- [Local Implicit Grid Representations for 3D Scenes](https://mlsanity.com/paper/2003.08981/) (2020)
- [Deep Joint Rain Detection and Removal from a Single Image](https://mlsanity.com/paper/1609.07769/) (2016)
- [Scale-recurrent Network for Deep Image Deblurring](https://mlsanity.com/paper/1802.01770/) (2018)
- [Rethinking Coarse-to-Fine Approach in Single Image Deblurring](https://mlsanity.com/paper/2108.05054/) (2021)
- [MAXIM: Multi-Axis MLP for Image Processing](https://mlsanity.com/paper/2201.02973/) (2022)
- [Progressive Image Deraining Networks: A Better and Simpler Baseline](https://mlsanity.com/paper/1901.09221/) (2019)
- [Multi-Scale Progressive Fusion Network for Single Image Deraining](https://mlsanity.com/paper/2003.10985/) (2020)
- [NeRV: Neural Representations for Videos](https://mlsanity.com/paper/2110.13903/) (2021)
- [Density-aware Single Image De-raining using a Multi-stream Dense Network](https://mlsanity.com/paper/1802.07412/) (2018)
- [Focal Frequency Loss for Image Reconstruction and Synthesis](https://mlsanity.com/paper/2012.12821/) (2020)
- [Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining](https://mlsanity.com/paper/1807.05698/) (2018)
- [Learning A Sparse Transformer Network for Effective Image Deraining](https://mlsanity.com/paper/2303.11950/) (2023)
- [Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset](https://mlsanity.com/paper/1904.01538/) (2019)
- [Implicit Neural Representations for Image Compression](https://mlsanity.com/paper/2112.04267/) (2021)
- [Local Texture Estimator for Implicit Representation Function](https://mlsanity.com/paper/2111.08918/) (2021)
- [A Model-driven Deep Neural Network for Single Image Rain Removal](https://mlsanity.com/paper/2005.01333/) (2020)
- [Implicit Neural Representation for Cooperative Low-light Image Enhancement](https://mlsanity.com/paper/2303.11722/) (2023)
- [Intriguing Findings of Frequency Selection for Image Deblurring](https://mlsanity.com/paper/2111.11745/) (2021)

## Built on by (most cited first, 30 of 54)

- [From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective](https://mlsanity.com/paper/2503.13165/) (2025)
- [NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results](https://mlsanity.com/paper/2504.12711/) (2025)
- [WeatherBench: A Real-World Benchmark Dataset for All-in-One Adverse Weather Image Restoration](https://mlsanity.com/paper/2509.11642/) (2025)
- [DeRainGS: Gaussian Splatting for Enhanced Scene Reconstruction in Rainy Environments](https://mlsanity.com/paper/2408.11540/) (2024)
- [Learning A Spiking Neural Network for Efficient Image Deraining](https://mlsanity.com/paper/2405.06277/) (2024)
- [Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining](https://mlsanity.com/paper/2503.18703/) (2025)
- [NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution](https://mlsanity.com/paper/2506.02197/) (2025)
- [Scan Clusters, Not Pixels: A Cluster-Centric Paradigm for Efficient Ultra-high-definition Image Restoration](https://mlsanity.com/paper/2602.21917/) (2026)
- [Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restoration](https://mlsanity.com/paper/2408.09241/) (2024)
- [EVOS: Efficient Implicit Neural Training via EVOlutionary Selector](https://mlsanity.com/paper/2412.10153/) (2024)
- [DSwinIR: Rethinking Window-based Attention for Image Restoration](https://mlsanity.com/paper/2504.04869/) (2025)
- [M2Restore: Mixture-of-Experts-based Mamba-CNN Fusion Framework for All-in-One Image Restoration](https://mlsanity.com/paper/2506.07814/) (2025)
- [A Hybrid Transformer-Mamba Network for Single Image Deraining](https://mlsanity.com/paper/2409.00410/) (2024)
- [Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration](https://mlsanity.com/paper/2504.09973/) (2025)
- [Rethinking Nighttime Image Deraining via Learnable Color Space Transformation](https://mlsanity.com/paper/2510.17440/) (2025)
- [Continuous Representation Methods, Theories, and Applications: An Overview and Perspectives](https://mlsanity.com/paper/2505.15222/) (2025)
- [SmokeBench: A Real-World Dataset for Surveillance Image Desmoking in Early-Stage Fire Scenes](https://mlsanity.com/paper/2509.12701/) (2025)
- [Rethinking Rainy 3D Scene Reconstruction via Perspective Transforming and Brightness Tuning](https://mlsanity.com/paper/2511.06734/) (2025)
- [Seeing Through the Rain: Resolving High-Frequency Conflicts in Deraining and Super-Resolution via Diffusion Guidance](https://mlsanity.com/paper/2511.12419/) (2025)
- [OPERA: An Agent for Image Restoration with End-to-End Joint Planning-Execution Optimization](https://mlsanity.com/paper/2605.22104/) (2026)
- [DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model](https://mlsanity.com/paper/2504.17732/) (2025)
- [Global Modeling Matters: A Fast, Lightweight and Effective Baseline for Efficient Image Restoration](https://mlsanity.com/paper/2507.13663/) (2025)
- [Zero-Shot Video Deraining with Video Diffusion Models](https://mlsanity.com/paper/2511.18537/) (2025)
- [Combined Flicker-banding and Moire Removal for Screen-Captured Images](https://mlsanity.com/paper/2602.01559/) (2026)
- [UniRain: Unified Image Deraining with RAG-based Dataset Distillation and Multi-objective Reweighted Optimization](https://mlsanity.com/paper/2603.03967/) (2026)
- [Unpaired Image Deraining Using Reward-Guided Self-Reinforcement Strategy](https://mlsanity.com/paper/2605.00719/) (2026)
- [Dual-Path Multi-Scale Transformer for High-Quality Image Deraining](https://mlsanity.com/paper/2405.18124/) (2024)
- [Learning Dual-Domain Multi-Scale Representations for Single Image Deraining](https://mlsanity.com/paper/2503.12014/) (2025)
- [Cross Paradigm Representation and Alignment Transformer for Image Deraining](https://mlsanity.com/paper/2504.16455/) (2025)
- [CLIP-driven rain perception: Adaptive deraining with pattern-aware network routing and mask-guided cross-attention](https://mlsanity.com/paper/2506.01366/) (2025)

## Abstract (arXiv)

How to effectively explore multi-scale representations of rain streaks is important for image deraining. In contrast to existing Transformer-based methods that depend mostly on single-scale rain appearance, we develop an end-to-end multi-scale Transformer that leverages the potentially useful features in various scales to facilitate high-quality image reconstruction. To better explore the common degradation representations from spatially-varying rain streaks, we incorporate intra-scale implicit neural representations based on pixel coordinates with the degraded inputs in a closed-loop design, enabling the learned features to facilitate rain removal and improve the robustness of the model in complex scenarios. To ensure richer collaborative representation from different scales, we embed a simple yet effective inter-scale bidirectional feedback operation into our multi-scale Transformer by performing coarse-to-fine and fine-to-coarse information communication. Extensive experiments demonstrate that our approach, named as NeRD-Rain, performs favorably against the state-of-the-art ones on both synthetic and real-world benchmark datasets. The source code and trained models are available at https://github.com/cschenxiang/NeRD-Rain.