Xiaoyuan Yang

Bio: Xiaoyuan Yang is an academic researcher from Beihang University. The author has contributed to research in topics: Robustness (computer science) & Second-generation wavelet transform. The author has an hindex of 3, co-authored 9 publications receiving 31 citations.

Random Walks for Synthetic Aperture Radar Image Fusion in Framelet Domain

Abstract: A new framelet-based random walks (RWs) method is presented for synthetic aperture radar (SAR) image fusion, including SAR-visible images, SAR-infrared images, and Multi-band SAR images. In this method, we build a novel RWs model based on the statistical characteristics of framelet coefficients to fuse the high-frequency and low-frequency coefficients. This model converts the fusion problem to estimate the probability of each framelet coefficient being assigned each input image. Experimental results show that the proposed approach improves the contrast while preserves the edges simultaneously, and outperforms many traditional and state-of-the-art fusion techniques in both qualitative and quantitative analysis.

Random Walks for Pansharpening in Complex Tight Framelet Domain

Abstract: In this paper, a new random walk (RW) pansharpening method on the basis of the complex framelet domain is proposed. In the process of fusion, the hidden Markov tree model is first established based on the statistical properties of complex high-pass framelet coefficients. On this basis, a novel RW fusion algorithm is presented. Then, the probabilities of complex framelet coefficients being allotted original images are solved by the linear system of equations. Based on these probabilities, the spatial details of the panchromatic image are selectively injected into the multispectral (MS) image to get a space-enhanced MS image. In the end, the GeoGye-1, WorldView-3, and WorldView-2 remote sensing image data sets are used to evaluate the performance of the presented method quantitatively and qualitatively. The results of the experiment show that our method outperforms some state-of-the-art approaches. It can improve the spatial resolution of the MS image while keeping the spectral information.

Lifting construction based on Bernstein bases and application in image compression

Abstract: A novel framework for the construction of biorthogonal wavelets based on Bernstein bases with an arbitrary order of vanishing moments using the lifting scheme is proposed. We explore the field of application of it in still image compression. The major contributions of this work can be summarised highlighting the following three aspects. First and foremost, we propose an algorithm that is used to increase the vanishing moments of wavelets from biorthogonal symmetrical wavelets based on Bernstein bases by the lifting scheme. An iterative algorithm for designing the lifting scheme is proposed, which is based on the relationship between the vanishing moments of the wavelet and multiples of zeros of z = 1. The authors provide formulas of the lifting scheme for the construction of wavelets with an arbitrary order of vanishing moments. In addition, the lifting scheme is the shortest among the lifting schemes with the same order of vanishing moments increasing and, more importantly, it is the only one possible. Second, to guarantee the symmetry of the lifting (dual lifting) biorthogonal filters, explicit formulas of the lifting scheme with an arbitrary order of vanishing moments are introduced, which simultaneously have the above two characteristics. With our method, a new family of the parameterisation with symmetry of filters and the related library of biorthogonal symmetric waveforms are presented. Finally, we present a new transform rule aiming at image compression and its corresponding algorithm. Applying the parameterisation of filters constructed in this paper, by adjusting their coefficients, we can realise the transform rule and obtain a new transform. We explore the possibility of applying the presented transforms in image compression at different compression rates, and the results of the experiments prove to be comparable with the CDF9/7 and several state-of-the-art wavelet transforms.

VIF-Net: An Unsupervised Framework for Infrared and Visible Image Fusion

Abstract: Visible images provide abundant texture details and environmental information, while infrared images benefit from night-time visibility and suppression of highly dynamic regions; it is a meaningful task to fuse these two types of features from different sensors to generate an informative image. In this article, we propose an unsupervised end-to-end learning framework for infrared and visible image fusion. We first construct enough benchmark training datasets using the visible and infrared frames, which can address the limitation of the training dataset. Additionally, due to the lack of labeled datasets, our architecture is derived from a robust mixed loss function that consists of the modified structural similarity (M-SSIM) metric and the total variation (TV) by designing an unsupervised learning process that can adaptively fuse thermal radiation and texture details and suppress noise interference. In addition, our method is an end to end model, which avoids setting hand-crafted fusion rules and reducing computational cost. Furthermore, extensive experimental results demonstrate that the proposed architecture performs better than state-of-the-art methods in both subjective and objective evaluations.

A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function

Abstract: Pansharpening is an important application in remote sensing image processing. It can increase the spatial-resolution of a multispectral image by fusing it with a high spatial-resolution panchromatic image in the same scene, which brings great favor for subsequent processing such as recognition, detection, etc . In this paper, we propose a continuous modeling and sparse optimization based method for the fusion of a panchromatic image and a multispectral image. The proposed model is mainly based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). In addition, we also propose a Toeplitz sparse term for representing the correlation of adjacent bands. The model is convex and solved by the alternating direction method of multipliers which guarantees the convergence of the proposed method. Extensive experiments on many real datasets collected by different sensors demonstrate the effectiveness of the proposed technique as compared with several state-of-the-art pansharpening approaches.

Applications, databases and open computer vision research from drone videos and images: a survey

Abstract: Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an emerging application attracting significant attention from researchers in various areas of computer vision. Currently, the major challenge is the development of autonomous operations to complete missions and replace human operators. In this paper, based on the type of analyzing videos and images captured by drones in computer vision, we have reviewed these applications by categorizing them into three groups. The first group is related to remote sensing with challenges such as camera calibration, image matching, and aerial triangulation. The second group is related to drone-autonomous navigation, in which computer vision methods are designed to explore challenges such as flight control, visual localization and mapping, and target tracking and obstacle detection. The third group is dedicated to using images and videos captured by drones in various applications, such as surveillance, agriculture and forestry, animal detection, disaster detection, and face recognition. Since most of the computer vision methods related to the three categories have been designed for real-world conditions, providing real conditions based on drones is impossible. We aim to explore papers that provide a database for these purposes. In the first two groups, some survey papers presented are current. However, the surveys have not been aimed at exploring any databases. This paper presents a complete review of databases in the first two groups and works that used the databases to apply their methods. Vision-based intelligent applications and their databases are explored in the third group, and we discuss open problems and avenues for future research.