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Sensors 2018, 18(11), 3827;

Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model

1,2, 1, 1, 1 and 1,*
Electronic Information School, Wuhan University, Wuhan 430072, China
Air Force Early Warning Academy, Wuhan 430019, China
Author to whom correspondence should be addressed.
Received: 9 September 2018 / Revised: 3 November 2018 / Accepted: 5 November 2018 / Published: 8 November 2018
(This article belongs to the Special Issue Sensors Signal Processing and Visual Computing)
PDF [7449 KB, uploaded 8 November 2018]


In infrared and visible image fusion, existing methods typically have a prerequisite that the source images share the same resolution. However, due to limitations of hardware devices and application environments, infrared images constantly suffer from markedly lower resolution compared with the corresponding visible images. In this case, current fusion methods inevitably cause texture information loss in visible images or blur thermal radiation information in infrared images. Moreover, the principle of existing fusion rules typically focuses on preserving texture details in source images, which may be inappropriate for fusing infrared thermal radiation information because it is characterized by pixel intensities, possibly neglecting the prominence of targets in fused images. Faced with such difficulties and challenges, we propose a novel method to fuse infrared and visible images of different resolutions and generate high-resolution resulting images to obtain clear and accurate fused images. Specifically, the fusion problem is formulated as a total variation (TV) minimization problem. The data fidelity term constrains the pixel intensity similarity of the downsampled fused image with respect to the infrared image, and the regularization term compels the gradient similarity of the fused image with respect to the visible image. The fast iterative shrinkage-thresholding algorithm (FISTA) framework is applied to improve the convergence rate. Our resulting fused images are similar to super-resolved infrared images, which are sharpened by the texture information from visible images. Advantages and innovations of our method are demonstrated by the qualitative and quantitative comparisons with six state-of-the-art methods on publicly available datasets. View Full-Text
Keywords: image fusion; different resolutions; total variation; infrared image fusion; different resolutions; total variation; infrared

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Du, Q.; Xu, H.; Ma, Y.; Huang, J.; Fan, F. Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model. Sensors 2018, 18, 3827.

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