Bam

Jin, Zi-Rong; Deng, Liang-Jian; Zhang, Tian-Jing; Jin, Xiao-Xu

doi:10.1145/3474085.3475571

Cited by 28 publications

(4 citation statements)

References 32 publications

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“…Ref. [24], Induison [26] from MRA, FE-HPM [30], PWMBF [31] from SR and six deep learning-based approaches, such as PNN [12], DRPNN [17], PercepPan [18], PGMAN [19], BAM [13], and MC-JAFN [14]. We reimplemented the BAM, MC-JAFN, DRPNN, PercepPan and PGMAN by Pytorch, and the rest of the methods were based on the MATLAB platform [39].…”

Section: Compared Methodsmentioning

confidence: 99%

“…For some SL-based algorithms, Masi et al [12] proposed PNN by interpolating the LRMS image with the PAN image. Jin et al [13] proposed a simple and effective bilateral activation mechanism (BAM) to avoid simply performing a negative truncation. However, the HRMS images will suffer from unreasonable artifacts in the training phase.…”

Section: Network Backbone For Pansharpeningmentioning

confidence: 99%

“…In remote sensing images, several researchers employed a convolutional neural network (CNN) to deal with panchromatic image sharpening, where it can effectively extract multi-level automatically. Most existing methods employ supervised learning (SL) to achieve pansharpening since the HRMS can be constrained by Ground Truth (GT), such as PNN [12], BAM [13], and MC-JAFN [14]. Nevertheless, on the one hand, the HRMS will produce artifacts because the GT is synthesized manually.…”

Section: Introductionmentioning

confidence: 99%

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DPAFNet: A Multistage Dense-Parallel Attention Fusion Network for Pansharpening

et al. 2022

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Pansharpening is the technology to fuse a low spatial resolution MS image with its associated high spatial full resolution PAN image. However, primary methods have the insufficiency of the feature expression and do not explore both the intrinsic features of the images and correlation between images, which may lead to limited integration of valuable information in the pansharpening results. To this end, we propose a novel multistage Dense-Parallel attention fusion network (DPAFNet). The proposed parallel attention residual dense block (PARDB) module can focus on the intrinsic features of MS images and PAN images while exploring the correlation between the source images. To fuse more complementary information as much as possible, the features extracted from each PARDB are fused at multistage levels, which allows the network to better focus on and exploit different information. Additionally, we propose a new loss, where it calculates the L2-norm between the pansharpening results and PAN images to constrain the spatial structures. Experiments were conducted on simulated and real datasets and the evaluation results verified the superiority of the DPAFNet.

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Section: Compared Methodsmentioning

confidence: 99%

Section: Network Backbone For Pansharpeningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DPAFNet: A Multistage Dense-Parallel Attention Fusion Network for Pansharpening

et al. 2022

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“…Wu et al [58] proposed an end-to-end fusion network architecture (RFN-Nest) to tackle the challenging problem of designing an appropriate strategy for generating fused images, incorporating a residual fusion network, detail-preserving and feature-enhancing loss functions, and a two-stage training strategy including an auto-encoder and the RFN. Z-R. Jin et al [59] proposed a simple but effective bilateral activation mechanism (BAM) which can be applied to the activation function to offer an efficient feature extraction model. It also introduces a Bilateral ReLU Residual Block (BRRB) and a BRResNet architecture to achieve state-of-the-art performance in two-image fusion tasks, i.e., pansharpening and hyperspectral image super-resolution (HISR).…”

Section: Typical Image Fusion Modelmentioning

confidence: 99%

Lightweight Infrared and Visible Image Fusion via Adaptive DenseNet with Knowledge Distillation

Zhao

Juanli

et al. 2023

Electronics

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The fusion of infrared and visible images produces a complementary image that captures both infrared radiation information and visible texture structure details using the respective sensors. However, the current deep-learning-based fusion approaches mainly tend to prioritize visual quality and statistical metrics, leading to an increased model complexity and weight parameter sizes. To address these challenges, we propose a novel dual-light fusion approach using adaptive DenseNet with knowledge distillation to learn and compress from pre-existing fusion models, which achieves the goals of model compression through the use of hyperparameters such as the width and depth of the model network. The effectiveness of our proposed approach is evaluated on a new dataset comprising three public datasets (MSRS, M3FD, and LLVIP), and both qualitative and quantitative experimental results show that the distillated adaptive DenseNet model effectively matches the original fusion models’ performance with smaller model weight parameters and shorter inference times.

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