# Spatial-frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement arXiv: https://arxiv.org/abs/2404.17400 ยท HTML: https://mlsanity.com/paper/2404.17400/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 67 references that are in the map. - First posted on arXiv 26 Apr 2024. ## Builds on (most cited first, 27 of 27) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [Multi-Stage Progressive Image Restoration](https://mlsanity.com/paper/2102.02808/) (2021) - [Resolution-robust Large Mask Inpainting with Fourier Convolutions](https://mlsanity.com/paper/2109.07161/) (2021) - [Deep Retinex Decomposition for Low-Light Enhancement](https://mlsanity.com/paper/1808.04560/) (2018) - [Learning A Sparse Transformer Network for Effective Image Deraining](https://mlsanity.com/paper/2303.11950/) (2023) - [iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images](https://mlsanity.com/paper/1905.12886/) (2019) - [Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation](https://mlsanity.com/paper/2103.00860/) (2021) - [Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method](https://mlsanity.com/paper/2212.11548/) (2022) - [Deep Generalized Unfolding Networks for Image Restoration](https://mlsanity.com/paper/2204.13348/) (2022) - [Implicit Neural Representation for Cooperative Low-light Image Enhancement](https://mlsanity.com/paper/2303.11722/) (2023) - [Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement](https://mlsanity.com/paper/2302.11831/) (2023) - [Intriguing Findings of Frequency Selection for Image Deblurring](https://mlsanity.com/paper/2111.11745/) (2021) - [Fourier Space Losses for Efficient Perceptual Image Super-Resolution](https://mlsanity.com/paper/2106.00783/) (2021) - [FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information](https://mlsanity.com/paper/2308.03033/) (2023) - [Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network](https://mlsanity.com/paper/2308.08220/) (2023) - [Learning Deep Context-Sensitive Decomposition for Low-Light Image Enhancement](https://mlsanity.com/paper/2112.05147/) (2021) - [Bilevel Fast Scene Adaptation for Low-Light Image Enhancement](https://mlsanity.com/paper/2306.01343/) (2023) - [Empowering Low-Light Image Enhancer through Customized Learnable Priors](https://mlsanity.com/paper/2309.01958/) (2023) - [Learning to Adapt to Light](https://mlsanity.com/paper/2202.08098/) (2022) - [Better Than Reference In Low Light Image Enhancement: Conditional Re-Enhancement Networks](https://mlsanity.com/paper/2008.11434/) (2020) - [Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing](https://mlsanity.com/paper/2308.06998/) (2023) - [Global Learnable Attention for Single Image Super-Resolution](https://mlsanity.com/paper/2212.01057/) (2022) - [Guided Hybrid Quantization for Object detection in Multimodal Remote Sensing Imagery via One-to-one Self-teaching](https://mlsanity.com/paper/2301.00131/) (2022) - [High-Similarity-Pass Attention for Single Image Super-Resolution](https://mlsanity.com/paper/2305.15768/) (2023) - [Seeing Text in the Dark: Algorithm and Benchmark](https://mlsanity.com/paper/2404.08965/) (2024) - [Low-Light Image Enhancement by Learning Contrastive Representations in Spatial and Frequency Domains](https://mlsanity.com/paper/2303.13412/) (2023) ## Built on by (most cited first, 7 of 7) - [HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning](https://mlsanity.com/paper/2411.18296/) (2024) - [GM-MoE: Low-Light Enhancement with Gated-Mechanism Mixture-of-Experts](https://mlsanity.com/paper/2503.07417/) (2025) - [Expose Camouflage in the Water: Underwater Camouflaged Instance Segmentation and Dataset](https://mlsanity.com/paper/2510.17585/) (2025) - [Integrating Spatial and Frequency Information for Under-Display Camera Image Restoration](https://mlsanity.com/paper/2501.18517/) (2025) - [From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration](https://mlsanity.com/paper/2505.22284/) (2025) - [DeepRFTv2: Kernel-level Learning for Image Deblurring](https://mlsanity.com/paper/2511.21132/) (2025) - [Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning](https://mlsanity.com/paper/2604.21349/) (2026) ## Abstract (arXiv) Low-light remote sensing images generally feature high resolution and high spatial complexity, with continuously distributed surface features in space. This continuity in scenes leads to extensive long-range correlations in spatial domains within remote sensing images. Convolutional Neural Networks, which rely on local correlations for long-distance modeling, struggle to establish long-range correlations in such images. On the other hand, transformer-based methods that focus on global information face high computational complexities when processing high-resolution remote sensing images. From another perspective, Fourier transform can compute global information without introducing a large number of parameters, enabling the network to more efficiently capture the overall image structure and establish long-range correlations. Therefore, we propose a Dual-Domain Feature Fusion Network (DFFN) for low-light remote sensing image enhancement. Specifically, this challenging task of low-light enhancement is divided into two more manageable sub-tasks: the first phase learns amplitude information to restore image brightness, and the second phase learns phase information to refine details. To facilitate information exchange between the two phases, we designed an information fusion affine block that combines data from different phases and scales. Additionally, we have constructed two dark light remote sensing datasets to address the current lack of datasets in dark light remote sensing image enhancement. Extensive evaluations show that our method outperforms existing state-of-the-art methods. The code is available at https://github.com/iijjlk/DFFN.