UFA-FUSE: A Novel Deep Supervised and Hybrid Model for Multifocus Image Fusion

Zang, Yongsheng; Zhou, Dongming; Wang, Changcheng; Nie, Rencan; Guo, Yanbu

doi:10.1109/tim.2021.3072124

Cited by 20 publications

(6 citation statements)

References 54 publications

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“…The compared methods include IFCNN [13], UFA-FUSE [17], MMF-Net [18], MFF-GAN [19], U2Fusion [14], GDF [20], ASR [21], and SFMD [22]. To obtain the best fusion results of each method, the parameters of all experiments were set to the default values according to the values given in the original paper.The nine metrics are used in the paper, which include Q_NMI [23],Q_G [24], Q_Y [25], Q_CB [26], Q_TE [27], Q_NCIE [28], Q_SG [29], Q_P [30], and Q_abf [31].…”

Section: Resultsmentioning

confidence: 99%

Multi-focus image fusion method based on multi-scale deep supervision

Jiang,

Wei

2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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Multi-focus image technology can be used to fuse a fully focused image by using multiple photos taken of the same scene at different focuses. The existing multi-focus image fusion methods have some common problems, such as large computation effort, incorrect estimate of focusing area, unclear fusion boundaries and edge contour loss, etc. To address these problems, an end-to-end fusion framework for multi-focus image fusion is proposed in the paper. Then, to obtain more information from source images, three parallel dilation convolutions are used to extract features from different scales in the paper. In addition, the manually designed fusion rules are replaced by an adaptive attention mechanism in the feature fusion stage. Finally, the final fusion results are reconstructed by convolution modules. The proposed method is better than other existing ones in terms of subjective visual effects and objective quantitative evaluation, according to experimental results.

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Section: Resultsmentioning

confidence: 99%

Multi-focus image fusion method based on multi-scale deep supervision

Jiang,

Wei

2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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“…Convolutional block attention modules (CBAM) [33] stack both channel attention and spatial attention in series. Li et al [19] utilized the attention module to focus on discriminative regions for fusing infrared and visible images, while UFA-Fuse [36] introduced a novel and effective image fusion strategy based on unity fusion attention. Inspired by the work above, we also propose a spatial temporal attention module to fuse multi-frame features.…”

Section: Related Workmentioning

confidence: 99%

FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

Chen,

Wei,

Liu

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://github.com/chenyirui/FASTC.

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“…To access the performance of our fusion model, a comparison with 6 typical fusion algorithms is conducted. Specifically, the compared algorithms contain RFN-Nest [10], UFA-FUSE [18], Image Fusion Transformer [19], CVT [25], MSVD [20] and WT [26]. The qualitative results…”

Section: Qualitative Assessmentsmentioning

confidence: 99%

DCAN: A Dual Cascade Attention Network for Fusing Pet and MRI Images

Liu

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Traditional fusion approaches and most deep learning-based methods usually generate the intermediate decision map, resulting in detail loss of source images or fusion results. In this work, to enhance the detailed features and structured information from source images, we propose a dual cascade attention network (DCAN) to obtain a more informative fusion image for PET and MRI images. In our approach, channel attention is employed to improve the ability of features representation and spatial attention can highlight informative regions in the proposed fusion network. Additionally, channel and spatial attention are sequential arrangement in channel-first. Moreover, to achieve good performance in the procedure of feature extraction and image reconstruction, two-stage training strategy is adopted to train our fusion model. Experimental results demonstrate that the proposed approach achieves remarkable performance for PET and MRI images fusion.

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UFA-FUSE: A Novel Deep Supervised and Hybrid Model for Multifocus Image Fusion

Cited by 20 publications

References 54 publications

Multi-focus image fusion method based on multi-scale deep supervision

Multi-focus image fusion method based on multi-scale deep supervision

FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

DCAN: A Dual Cascade Attention Network for Fusing Pet and MRI Images

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