Deep Cross Spectral Stereo Matching Using Multi-Spectral Image Fusion

Liang, Xiaolong; Jung, Cheolkon

doi:10.1109/lra.2022.3155202

Cited by 11 publications

(1 citation statement)

References 37 publications

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“…Liang et al improved Zhi’s network structure in 2019 [ 13 ] by using a spectrally adversarial transformation network (F-cycleGAN) to enhance the quality of disparity prediction. In 2022, Liang et al added a multispectral fusion subnetwork to the previous two network architectures [ 14 ], minimizing cross-spectral differences between visible light and near-infrared images through fusion. The aforementioned networks are more suitable for visible light–near-infrared image pairs with minor spectral differences; however, their performance is not ideal for visible light–thermal infrared image pairs with more significant spectral differences.…”

Section: Related Workmentioning

confidence: 99%

Iterative Network for Disparity Prediction with Infrared and Visible Light Images Based on Common Features

Zhang,

Li,

Jin

et al. 2023

Sensors

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In recent years, the range of applications that utilize multiband imaging has significantly expanded. However, it is difficult to utilize multichannel heterogeneous images to achieve a spectral complementarity advantage and obtain accurate depth prediction based on traditional systems. In this study, we investigate CFNet, an iterative prediction network, for disparity prediction with infrared and visible light images based on common features. CFNet consists of several components, including a common feature extraction subnetwork, context subnetwork, multimodal information acquisition subnetwork, and a cascaded convolutional gated recurrent subnetwork. It leverages the advantages of dual-band (infrared and visible light) imaging, considering semantic information, geometric structure, and local matching details within images to predict the disparity between heterogeneous image pairs accurately. CFNet demonstrates superior performance in recognized evaluation metrics and visual image observations when compared with other publicly available networks, offering an effective technical approach for practical heterogeneous image disparity prediction.

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Section: Related Workmentioning

confidence: 99%