Infrared and visible image fusion aims to describe the same scene from different aspects by combining complementary information of multi-modality images. The existing Generative adversarial networks (GAN) based infrared and visible image fusion methods cannot perceive the most discriminative regions, and hence fail to highlight the typical parts existing in infrared and visible images. To this end, we integrate multi-scale attention mechanism into both generator and discriminator of GAN to fuse infrared and visible images (AttentionFGAN). The multi-scale attention mechanism aims to not only capture comprehensive spatial information to help generator focus on the foreground target information of infrared image and background detail information of visible image, but also constrain the discriminators focus more on the attention regions rather than the whole input image. The generator of AttentionFGAN consists of two multi-scale attention networks and an image fusion network. Two multi-scale attention networks capture the attention maps of infrared and visible images respectively, so that the fusion network can reconstruct the fused image by paying more attention to the typical regions of source images. Besides, two discriminators are adopted to force the fused result keep more intensity and texture information from infrared and visible image respectively. Moreover, to keep more information of attention region from source images, an attention loss function is designed. Finally, the ablation experiments illustrate the effectiveness of the key parts of our method, and extensive qualitative and quantitative experiments on three public datasets demonstrate the advantages and effectiveness of AttentionFGAN compared with the other state-of-the-art methods.

Methods based on generative adversarial network (GAN) have been widely used in infrared and visible images fusion. However, these methods cannot perceive the discriminative parts of an image. Therefore, we introduce a multi-grained attention module into encoder-decoder network to fuse infrared and visible images (MgAN-Fuse). The infrared and visible images are encoded by two independent encoder networks due to their diverse modalities. Then, the results of the two encoders are concatenated to calculate the fused result by the decoder. To exploit the features of multi-scale layers fully and force the model focus on the discriminative regions, we integrate attention modules into multi-scale layers of the encoder to obtain multi-grained attention maps, and then the multi-grained attention maps are concatenated with the corresponding multi-scale features of decoder network. Thus, the proposed method can preserve the foreground target information of the infrared image, and capture the context information of the visible image. Furthermore, we design an additional feature loss in the training process to preserve the important features of the visible image, and a dual adversarial architecture is employed to help the model capture enough infrared intensity information and visible details simultaneously. The ablation studies illustrate the validity of multi-grained attention network and feature loss function. Extensive experiments on two infrared and visible image datasets demonstrate that the proposed MgAN-Fuse has better performance than state-of-the-art methods.

The capability of a radar system to detect and identify the low-slow-small rotor unmanned aerial vehicle (UAV) is intensely important for management and control in low altitude, and it can be enhanced by the characteristics of UAV, which inherently carries micro-Doppler information. In this paper, we establish a micro-motion model of rotor UAV, and analyze the range walking and Doppler broadening caused by micro-motion. Hence, based on the characteristics of micro-Doppler of the rotors apart from the main body for the moving UAV, a micro-motion target detection and micro-Doppler parameters estimation method is proposed. Firstly, a method based on datashift in range dimension is used to compensate the translational component of the main body. Secondly, for the echo after translation compensation, a parameter estimation method of micro-Doppler based on the optimal demodulation operator is proposed. The operator contents the maximum likelihood criterion, which strengthens the difference between the micro-Doppler signal and the background clutters, so that more signal energy can be accumulated while the clutter is suppressed. Subsequently, the micro-Doppler parameters of the micro-motion component are obtained by the optimal demodulator, so that the detection and identification of the target are realized. Additionally, the performance of proposed method, including parameter estimation, signal-to-noise ratio (SNR) improvement and the calculation efficiency is analyzed. Finally, the simulation and experimental results reveal that the proposed method does possess the capability to improve the performance of detection and recognition of rotor UAV.

Saliency-based methods have been widely used in the fusion of infrared (IR) and visible (VIS) images, which can highlight the salient object region and preserve the detailed background information simultaneously. However, most existing methods ignore the salient information in visible image or they fail to highlight the boundaries of objects, which makes the final saliency map incomplete and the edges of the object blurred. To address the above issues, we propose a novel infrared and visible images fusion algorithm based on Poisson reconstruction and saliency detection using the Dempster-Shafer theory (DS). In detail, we mix the gradient using a mask map derived from the saliency map, which could avoid low contrast and halo effects in the results. Besides, both the intensity saliency of the infrared image and the structural saliency of all source images are considered by DS to suppress some noise in the infrared image. Thus, we could obtain smooth object contours and enhance the edge information of the salient region. Moreover, we also propose a novel probability mass function to calculate the probabilistic map in the process of applying DS to decrease the error from manually assigning the prior probability. Finally, extensive qualitative and quantitative experiments have demonstrated the advantages and effectiveness of our method compared with other nine state-of-the-art infrared and visible image fusion methods.

Frame deletion detection is an arrestive field for video forensics in recent years. This paper presents a frame deletion detection algorithm based on optical flow orientation variation. Optical flow field has been proved to be effective for the detection of the frame deletion operation. However, current optical-flow based methods have relatively poor detection performance for real-world videos. Our research aims to develop a new forensic method to detect frame deletion for real-world videos. The proposed algorithm firstly develops an analytical model for flow orientation variation between adjacent optical flow fields, and exhibits a novel forensic clue for frame deletion detection, under an ideal but reasonable assumption. Accordingly, an effective descriptor, namely pseudo flow orientation variation (PFOV), is created to approximate the flow orientation variation, as a discriminative description feature to capture the frame deletion trace. The Frobenius norm and a smooth function are also introduced to quantify each descriptor to form an one-dimensional time series, and further statistics technique is adopted to detect the anomalous points in the time series caused by frame deletion. In addition, to improve the descriptor performance, the Robust Principal Component Analysis (RPCA) is applied to extract moving objects from video sequences and compute the proposed descriptor on them. The tests are made on 324 real-world videos, and experiment results demonstrate that the true positive rate can reach 90.12%, meanwhile the false alarm rate is 7.71%, which indicates the superiority of the proposed method in the forensic algorithms for real-world video.