The TNO Multiband Image Data Collection

Toet, Alexander

doi:10.1016/j.dib.2017.09.038

Cited by 163 publications

(60 citation statements)

References 3 publications

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“…We evaluate MIFFuse on a publicly accessible dataset(TNO [37] and CVC-14 [38]) and compare it to other deep learning-based methods in this section. Those methods including CSR [39], DenseFuse [27], FusionGAN [30], IFCNN [29], SEDRFuse [28].…”

Section: Resultsmentioning

confidence: 99%

MIFFuse: A Multi-Level Feature Fusion Network for Infrared and Visible Images

Zhu¹,

Zhan

Jiang

et al. 2021

IEEE Access

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Image fusion operation is beneficial to many applications and is also one of the most common and critical computer vision challenges. The perfect infrared and visible image fusion results should include the important infrared targets while preserving visible textural detail information as much as possible. A novel infrared and visible image fusion framework is proposed for this purpose. In this paper, the proposed fusion network (MIFFuse) is an end-to-end, multi-level-based fusion network for infrared and visible images. The presented approach makes effective use of the intermediate convolution layer's output features to preserve the primary image fusion information. We also build a cat_block to swap information between two paths to gain more sufficient information during the convolution steps. To reduce the model's running time even further, the proposed method that reduces the number of feature channels while maintaining the accuracy of the fusion performance. Extensive experiments on the TNO and CVC-14 image fusion datasets show that our MIFFuse outperforms the other methods in terms of both subjective visual effects and quantitative metrics. Furthermore, MIFFuse is approximately twice as fast as the most recent state-ofthe-art methods. Our code and models can be found at https://github.com/depeng6/MIFFuse.INDEX TERMS end-to-end framework, multi-level features, image fusion, concatenation block.

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Section: Resultsmentioning

confidence: 99%

MIFFuse: A Multi-Level Feature Fusion Network for Infrared and Visible Images

Zhu¹,

Zhan

Jiang

et al. 2021

IEEE Access

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“…We implement our model on three benchmark datasets: CaliforniaND [ 3 ] and Mir-Flickr Near Duplicate (MFND) [ 4 ] for single-modality near duplicate image pairs comparing, while TNO Multi-band Image Data Collection (TNO) [ 41 ] for cross-modality near duplicate image pairs comparing.…”

Section: Methodsmentioning

confidence: 99%

Single- and Cross-Modality Near Duplicate Image Pairs Detection via Spatial Transformer Comparing CNN

Zhang

2021

Sensors

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Recently, both single modality and cross modality near-duplicate image detection tasks have received wide attention in the community of pattern recognition and computer vision. Existing deep neural networks-based methods have achieved remarkable performance in this task. However, most of the methods mainly focus on the learning of each image from the image pair, thus leading to less use of the information between the near duplicate image pairs to some extent. In this paper, to make more use of the correlations between image pairs, we propose a spatial transformer comparing convolutional neural network (CNN) model to compare near-duplicate image pairs. Specifically, we firstly propose a comparing CNN framework, which is equipped with a cross-stream to fully learn the correlation information between image pairs, while considering the features of each image. Furthermore, to deal with the local deformations led by cropping, translation, scaling, and non-rigid transformations, we additionally introduce a spatial transformer comparing CNN model by incorporating a spatial transformer module to the comparing CNN architecture. To demonstrate the effectiveness of the proposed method on both the single-modality and cross-modality (Optical-InfraRed) near-duplicate image pair detection tasks, we conduct extensive experiments on three popular benchmark datasets, namely CaliforniaND (ND means near duplicate), Mir-Flickr Near Duplicate, and TNO Multi-band Image Data Collection. The experimental results show that the proposed method can achieve superior performance compared with many state-of-the-art methods on both tasks.

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“…Conducted experiment results are presented in this section. In the following sections, quantitative and qualitative comparisons are explained using TNO [13] and VIFB [18] datasets.…”

Section: Methodsmentioning

confidence: 99%

Visible And Infrared Image Fusion Using Encoder-Decoder Network

Ataman¹,

Akar²

2021

2021 IEEE International Conference on Image Processing (ICIP)

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The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution utilizes only convolution and pooling layers together with a loss function using no-reference quality metrics. The analysis is performed qualitatively and quantitatively on various datasets. The results show better performance than stateof-the-art methods. Also, the size of our network enables real-time performance on embedded devices. Project codes can be found at https://github.com/ferhatcan/ pyFusionSR.

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The TNO Multiband Image Data Collection

Cited by 163 publications

References 3 publications

MIFFuse: A Multi-Level Feature Fusion Network for Infrared and Visible Images

MIFFuse: A Multi-Level Feature Fusion Network for Infrared and Visible Images

Single- and Cross-Modality Near Duplicate Image Pairs Detection via Spatial Transformer Comparing CNN

Visible And Infrared Image Fusion Using Encoder-Decoder Network

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