# EvaNet: Towards More Efficient and Consistent Infrared and Visible Image Fusion Assessment arXiv: https://arxiv.org/abs/2604.02896 ยท HTML: https://mlsanity.com/paper/2604.02896/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 60 references that are in the map. - First posted on arXiv 3 Apr 2026. ## Builds on (most cited first, 30 of 37) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014) - [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015) - [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023) - [Restormer: Efficient Transformer for High-Resolution Image Restoration](https://mlsanity.com/paper/2111.09881/) (2021) - [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022) - [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) - [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021) - [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016) - [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) - [PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers](https://mlsanity.com/paper/2206.02066/) (2022) - [Text-IF: Leveraging Semantic Text Guidance for Degradation-Aware and Interactive Image Fusion](https://mlsanity.com/paper/2403.16387/) (2024) - [FusionMamba: Dynamic Feature Enhancement for Multimodal Image Fusion with Mamba](https://mlsanity.com/paper/2404.09498/) (2024) - [CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion](https://mlsanity.com/paper/2211.10960/) (2022) - [MDLatLRR: A novel decomposition method for infrared and visible image fusion](https://mlsanity.com/paper/1811.02291/) (2018) - [MambaDFuse: A Mamba-based Dual-phase Model for Multi-modality Image Fusion](https://mlsanity.com/paper/2404.08406/) (2024) - [Every SAM Drop Counts: Embracing Semantic Priors for Multi-Modality Image Fusion and Beyond](https://mlsanity.com/paper/2503.01210/) (2025) - [DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2503.17673/) (2025) - [Generative-based Fusion Mechanism for Multi-Modal Tracking](https://mlsanity.com/paper/2309.01728/) (2023) - [One Model for ALL: Low-Level Task Interaction Is a Key to Task-Agnostic Image Fusion](https://mlsanity.com/paper/2502.19854/) (2025) - [TGFuse: An Infrared and Visible Image Fusion Approach Based on Transformer and Generative Adversarial Network](https://mlsanity.com/paper/2201.10147/) (2022) - [Learning a Graph Neural Network with Cross Modality Interaction for Image Fusion](https://mlsanity.com/paper/2308.03256/) (2023) - [TextFusion: Unveiling the Power of Textual Semantics for Controllable Image Fusion](https://mlsanity.com/paper/2312.14209/) (2023) ## Built on by (most cited first, 2 of 2) - [Bringing Multimodal Large Language Models to Infrared-Visible Image Fusion Quality Assessment](https://mlsanity.com/paper/2605.06969/) (2026) - [Right Answer, Wrong Heat: Explanation-Aware Evaluation and Thermal-Grounded Feedback for MLLMs on Infrared Images](https://mlsanity.com/paper/2608.09145/) (2026) ## Abstract (arXiv) Evaluation is essential in image fusion research, yet most existing metrics are directly borrowed from other vision tasks without proper adaptation. These traditional metrics, often based on complex image transformations, not only fail to capture the true quality of the fusion results but also are computationally demanding. To address these issues, we propose a unified evaluation framework specifically tailored for image fusion. At its core is a lightweight network designed efficiently to approximate widely used metrics, following a divide-and-conquer strategy. Unlike conventional approaches that directly assess similarity between fused and source images, we first decompose the fusion result into infrared and visible components. The evaluation model is then used to measure the degree of information preservation in these separated components, effectively disentangling the fusion evaluation process. During training, we incorporate a contrastive learning strategy and inform our evaluation model by perceptual scene assessment provided by a large language model. Last, we propose the first consistency evaluation framework, which measures the alignment between image fusion metrics and human visual perception, using both independent no-reference scores and downstream tasks performance as objective references. Extensive experiments show that our learning-based evaluation paradigm delivers both superior efficiency (up to 1,000 times faster) and greater consistency across a range of standard image fusion benchmarks. Our code will be publicly available at https://github.com/AWCXV/EvaNet.