# DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion

arXiv: https://arxiv.org/abs/2503.17673 · HTML: https://mlsanity.com/paper/2503.17673/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 49 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion](https://mlsanity.com/paper/2211.14461/) (2022)
- [DenseFuse: A Fusion Approach to Infrared and Visible Images](https://mlsanity.com/paper/1804.08361/) (2018)
- [DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion](https://mlsanity.com/paper/2303.06840/) (2023)
- [Multi-interactive Feature Learning and a Full-time Multi-modality Benchmark for Image Fusion and Segmentation](https://mlsanity.com/paper/2308.02097/) (2023)
- [RFN-Nest: An end-to-end residual fusion network for infrared and visible images](https://mlsanity.com/paper/2103.04286/) (2021)
- [Equivariant Multi-Modality Image Fusion](https://mlsanity.com/paper/2305.11443/) (2023)
- [LRRNet: A Novel Representation Learning Guided Fusion Network for Infrared and Visible Images](https://mlsanity.com/paper/2304.05172/) (2023)
- [Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion](https://mlsanity.com/paper/2403.16387/) (2024)
- [CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion](https://mlsanity.com/paper/2211.10960/) (2022)
- [A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion](https://mlsanity.com/paper/2305.15862/) (2023)
- [Infrared and Visible Image Fusion: From Data Compatibility to Task Adaption](https://mlsanity.com/paper/2501.10761/) (2025)

## Built on by (most cited first, 30 of 34)

- [Efficient Rectified Flow for Image Fusion](https://mlsanity.com/paper/2509.16549/) (2025)
- [A Unified Solution to Video Fusion: From Multi-Frame Learning to Benchmarking](https://mlsanity.com/paper/2505.19858/) (2025)
- [UniFusion: A Unified Image Fusion Framework with Robust Representation and Source-Aware Preservation](https://mlsanity.com/paper/2603.14214/) (2026)
- [Towards Unified Semantic and Controllable Image Fusion: A Diffusion Transformer Approach](https://mlsanity.com/paper/2512.07170/) (2025)
- [Bridging Human Evaluation to Infrared and Visible Image Fusion](https://mlsanity.com/paper/2603.03871/) (2026)
- [Collaborating Vision, Depth, and Thermal Signals for Multi-Modal Tracking: Dataset and Algorithm](https://mlsanity.com/paper/2509.24741/) (2025)
- [Enhancing Infrared Vision: Progressive Prompt Fusion Network and Benchmark](https://mlsanity.com/paper/2510.09343/) (2025)
- [DEPFusion: Dual-Domain Enhancement and Priority-Guided Mamba Fusion for UAV Multispectral Object Detection](https://mlsanity.com/paper/2509.07327/) (2025)
- [Toward Real-world Infrared Image Super-Resolution: A Unified Autoregressive Framework and Benchmark Dataset](https://mlsanity.com/paper/2603.04745/) (2026)
- [EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment](https://mlsanity.com/paper/2604.02896/) (2026)
- [MambaTrans: Multimodal Fusion Image Translation via Large Language Model Priors for Downstream Visual Tasks](https://mlsanity.com/paper/2508.07803/) (2025)
- [Rethinking Unsupervised Cross-modal Flow Estimation: Learning from Decoupled Optimization and Consistency Constraint](https://mlsanity.com/paper/2509.24423/) (2025)
- [3M-TI: High-Quality Mobile Thermal Imaging via Calibration-free Multi-Camera Cross-Modal Diffusion](https://mlsanity.com/paper/2511.19117/) (2025)
- [HATIR: Heat-Aware Diffusion for Turbulent Infrared Video Super-Resolution](https://mlsanity.com/paper/2601.04682/) (2026)
- [MambaVF: State Space Model for Efficient Video Fusion](https://mlsanity.com/paper/2602.06017/) (2026)
- [Multi-Modal Image Fusion via Intervention-Stable Feature Learning](https://mlsanity.com/paper/2603.23272/) (2026)
- [Uncertainty-aware Spatial-Frequency Registration and Fusion for Infrared and Visible Images](https://mlsanity.com/paper/2605.13049/) (2026)
- [From 2D Grids to 1D Tokens: Reforming Shared Representations for Multimodal Image Fusion](https://mlsanity.com/paper/2606.12303/) (2026)
- [EvReflection: Event-Driven Micro-Dynamics for Reflection Removal](https://mlsanity.com/paper/2608.06184/) (2026)
- [Serial Over Parallel: Learning Continual Unification for Multi-Modal Visual Object Tracking and Benchmarking](https://mlsanity.com/paper/2508.10655/) (2025)
- [Direction-aware multi-scale gradient loss for infrared and visible image fusion](https://mlsanity.com/paper/2510.13067/) (2025)
- [Missing No More: Dictionary-Guided Cross-Modal Image Fusion under Missing Infrared](https://mlsanity.com/paper/2603.08018/) (2026)
- [MAVFusion: Efficient Infrared and Visible Video Fusion via Motion-Aware Sparse Interaction](https://mlsanity.com/paper/2604.01958/) (2026)
- [EAPFusion: Intrinsic Evolving Auxiliary Prior Guidance for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2605.01916/) (2026)
- [Fusion in Your Way: Aligning Image Fusion with Heterogeneous Demands via Direct Preference Optimization](https://mlsanity.com/paper/2605.06049/) (2026)
- [Dual-Correlation Hypergraph Network for Unaligned RGBT Video Object Detection and A Large-scale Benchmark](https://mlsanity.com/paper/2607.08191/) (2026)
- [Current Injection Spiking Neural Network for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2607.19879/) (2026)
- [BeyondFusion: Self-Aligned Latent Diffusion for Calibration-Free Infrared Super-Resolution and Infrared-Visible Fusion](https://mlsanity.com/paper/2607.24110/) (2026)
- [ProtoHGF-Net: Prototype HyperGraph Fusion with Intra-modal Calibration for RGBT Object Detection](https://mlsanity.com/paper/2608.11595/) (2026)
- [P2Fusion: Prompt-based Progressive Infrared-Visible Image Fusion via Dual-Prior Distillation](https://mlsanity.com/paper/2608.13045/) (2026)

## Abstract (arXiv)

Infrared and visible image fusion integrates information from distinct spectral bands to enhance image quality by leveraging the strengths and mitigating the limitations of each modality. Existing approaches typically treat image fusion and subsequent high-level tasks as separate processes, resulting in fused images that offer only marginal gains in task performance and fail to provide constructive feedback for optimizing the fusion process. To overcome these limitations, we propose a Discriminative Cross-Dimension Evolutionary Learning Framework, termed DCEvo, which simultaneously enhances visual quality and perception accuracy. Leveraging the robust search capabilities of Evolutionary Learning, our approach formulates the optimization of dual tasks as a multi-objective problem by employing an Evolutionary Algorithm (EA) to dynamically balance loss function parameters. Inspired by visual neuroscience, we integrate a Discriminative Enhancer (DE) within both the encoder and decoder, enabling the effective learning of complementary features from different modalities. Additionally, our Cross-Dimensional Embedding (CDE) block facilitates mutual enhancement between high-dimensional task features and low-dimensional fusion features, ensuring a cohesive and efficient feature integration process. Experimental results on three benchmarks demonstrate that our method significantly outperforms state-of-the-art approaches, achieving an average improvement of 9.32% in visual quality while also enhancing subsequent high-level tasks. The code is available at https://github.com/Beate-Suy-Zhang/DCEvo.