Sustainable wearable infrared shielding bamboo fiber fabrics loaded with antimony doped tin oxide/silver binary nanoparticles

Advanced Composites and Hybrid Materials, Год журнала: 2023, Номер 6(3)

Опубликована: Май 18, 2023

Язык: Английский

---

A review of image fusion: Methods, applications and performance metrics

Digital Signal Processing, Год журнала: 2023, Номер 137, С. 104020 - 104020

Опубликована: Март 24, 2023

Язык: Английский

---

Fully-Connected Transformer for Multi-Source Image Fusion

IEEE Transactions on Pattern Analysis and Machine Intelligence, Год журнала: 2025, Номер 47(3), С. 2071 - 2088

Опубликована: Фев. 5, 2025

Multi-source image fusion combines the information coming from multiple images into one data, thus improving imaging quality. This topic has aroused great interest in community. How to integrate different sources is still a big challenge, although existing self-attention based transformer methods can capture spatial and channel similarities. In this paper, we first discuss mathematical concepts behind proposed generalized mechanism, where self-attentions are considered basic forms. The mechanism employs multilinear algebra drive development of novel fully-connected (FCSA) method fully exploit local non-local domain-specific correlations among multi-source images. Moreover, propose representation embedding it FCSA framework as prior within an optimization problem. Some problems unfolded network (FC-Former). More specifically, concept promote potential self-attention. Hence, FC-Former be viewed model unifying tasks. Compared with state-of-the-art methods, exhibits robust superior performance, showing its capability faithfully preserving information.

Язык: Английский

---

Sparse Representation-Based Multi-Focus Image Fusion Method via Local Energy in Shearlet Domain

Sensors, Год журнала: 2023, Номер 23(6), С. 2888 - 2888

Опубликована: Март 7, 2023

Multi-focus image fusion plays an important role in the application of computer vision. In process fusion, there may be blurring and information loss, so it is our goal to obtain high-definition information-rich images. this paper, a novel multi-focus method via local energy sparse representation shearlet domain proposed. The source images are decomposed into low- high-frequency sub-bands according transform. low-frequency fused by representation, energy. inverse transform used reconstruct image. Lytro dataset with 20 pairs verify proposed method, 8 state-of-the-art methods metrics for comparison. According experimental results, can generate good performance fusion.

Язык: Английский

---

A review of cancer data fusion methods based on deep learning

Information Fusion, Год журнала: 2024, Номер 108, С. 102361 - 102361

Опубликована: Март 20, 2024

Язык: Английский

---

Developments in Brain Tumor Segmentation Using MRI: Deep Learning Insights and Future Perspectives

IEEE Access, Год журнала: 2024, Номер 12, С. 26875 - 26896

Опубликована: Янв. 1, 2024

The human brain is an incredible and wonderful organ that governs all body actions. Due to its great importance, any defect in the shape of regions should be reported quickly reduce death rate. abnormal region segmentation helps plan monitor treatment. most critical procedure isolating normal tissues from each other. So far, remarkable imaging modalities are being used diagnose abnormalities at their early stages, magnetic resonance (MRI) renowned noninvasive among those modalities. This paper investigates current landscape tumor (BTS) by exploring emerging deep learning (DL) methods for MRI analysis. findings offer a comprehensive comparison recent DL approaches, emphasizing effectiveness handling diverse types while addressing limitations associated with data scarcity robust validation. has shown vital improvement BTS, so our primary focus include significant models analyze MRI. However, outperforms traditional methods; still, there several limitations, especially related types, lack datasets, weak validations. future perspectives DL-based BTS present potential revolutionizing diagnosis treatment tumors.

Язык: Английский

---

CEFusion: An Infrared and Visible Image Fusion Network Based on Cross-Modal Multi-Granularity Information Interaction and Edge Guidance

IEEE Transactions on Intelligent Transportation Systems, Год журнала: 2024, Номер 25(11), С. 17794 - 17809

Опубликована: Июль 19, 2024

Язык: Английский

---

Computer vision for wildfire detection: a critical brief review

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(35), С. 83427 - 83470

Опубликована: Март 13, 2024

Язык: Английский

---

A review on infrared and visible image fusion algorithms based on neural networks

Journal of Visual Communication and Image Representation, Год журнала: 2024, Номер 101, С. 104179 - 104179

Опубликована: Май 1, 2024

Infrared and visible image fusion represents a significant segment within the domain. The recent surge in processing hardware advancements, including GPUs, TPUs, cloud computing platforms, has facilitated of extensive datasets from multiple sensors. Given remarkable proficiency neural networks feature extraction fusion, their application infrared emerged as prominent research area years. This article begins by providing an overview current mainstream algorithms for based on networks, detailing principles various algorithms, representative works, respective advantages disadvantages. Subsequently, it introduces domain-relevant datasets, evaluation metrics, some typical scenarios. Finally, conducts qualitative quantitative evaluations results state-of-the-art offers future prospects experimental results.

Язык: Английский

---

Multi-Focus Image Fusion: A Systematic Literature Review

SN Computer Science, Год журнала: 2025, Номер 6(2)

Опубликована: Фев. 8, 2025

---