
Yong YangTiangong University · School of Computer Science and Technology
Yong Yang
PhD
My interests include image processing, pattern recognition, and deep learning.
About
220
Publications
39,065
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4,826
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Introduction
image fusion, image super-resolution, image restoration, and deep learning.
Additional affiliations
December 2021 - present
Tiangong University
Position
- Professor
March 2006 - November 2021
Education
March 2002 - December 2005
Publications
Publications (220)
Infrared and visible image fusion (IVIF) aims to fuse these two modal images to generate a single image with rich textures and clear targets. Most current deep learning-based fusion methods directly fuse the features of these two modal images, without fully considering their specific attributes, which causes the fusion image to be more inclined to...
Hyperspectral (HS) pansharpening aims to fuse high-spatial-resolution panchromatic (PAN) images with low-spatial-resolution hyperspectral (LRHS) images to generate high-spatial-resolution hyperspectral (HRHS) images. Due to the lack of consideration for the modal feature difference between PAN and LRHS images, most deep leaning-based methods suffer...
Face recognition has significantly improved with the development of deep learning technology. However, in the case of a viral epidemic like COVID-19, wearing masks reduces the risk of infection significantly but results in losing crucial face features and increasing intra-class divergence, which decreases the effectiveness and accuracy of face reco...
Image composition has always been one of the primary considerations for professional photography, and good composition can greatly enhance the perception of an image. However, it is difficult for most ordinary users to compose images quickly. To solve this challenge, we propose a new view adjustment method to improve image composition in this paper...
Multimodal summarization (MS) for videos aims to generate summaries from multi-source information (e.g., video and text transcript), showing promising progress recently. However, existing works are limited to monolingual scenarios, neglecting non-native viewers' needs to understand videos in other languages. It stimulates us to introduce multimodal...
Due to limited memory and computing resources, the application of deep neural networks on embedded and mobile devices is still a great challenge. To tackle this problem, this paper proposes a lightweight super-resolution network based on asymmetric encoder–decoder (LSRN-AED), which achieves better performance while reducing model parameters and com...
Methamphetamine (MA) is a neurological drug, which is harmful to the overall brain cognitive function when abused. Based on this property of MA, people can be divided into those with MA abuse and healthy people. However, few studies to date have investigated automatic detection of MA abusers based on the neural activity. For this reason, the purpos...
In recent years, some deep learning dehazing methods based on atmospheric scattering model mostly solve the dehazing results by using depth convolution neural networks (CNNs) to estimate the medium transmission map in the model. However, these methods usually ignored the potential correlation between the transmission map and the atmospheric light i...
Infrared and visible image fusion (IVIF) aims to fully preserve the target and detail information of the infrared and visible images in the fusion image. Although deep learning–based methods have been widely used in IVIF, they usually use the same network structure to extract features without considering the differences between different image moda...
Currently, single-image super-resolution (SISR) methods based on convolutional neural networks have achieved remarkable results. However, most methods improve the reconstruction performance of the network by increasing the depth and complexity of the network, which leads to an increase in the computation and storage of the network. To address this...
Infrared and visible image fusion (IVIF) is to achieve the fused images with multimodal complementary information of source images. To effectively fuse the complementary information, a dual-encoder network based on multi-layer feature fusion for IVIF is proposed, which can effectively fuse the features of source images at different levels. Specific...
Due to the unique environment and inherent properties of magnetic resonance imaging (MRI) instruments, MR images typically have lower resolution. Therefore, improving the resolution of MR images is beneficial for assisting doctors in diagnosing the condition. Currently, the existing MR image super-resolution (SR) methods still have the problem of i...
Images captured in low brightness environment have issues with low contrast and noise due to uneven lighting, which can seriously affect the accuracy of high-level computer vision tasks. Currently, most enhancement methods still suffer from color distortion and noise amplification. To overcome these issues, this paper proposes an illumination-aware...
Multi-exposure image fusion (MEF) technology aims to extract visible information in image sequences of the same scene with different exposure levels, and combines the extracted information to generate a clear and visible composite image. In traditional methods, constructing weight maps and extracting fine details are two core issues. To overcome th...
Pansharpening technology aims to extract spatial information from a panchromatic (PAN) image and integrate it into a multispectral (MS) image to generate a high-spatial resolution MS image. To overcome the problem of spatial and spectral distortion of traditional methods, this letter presents a pansharpening method based on fuzzy logic and edge act...
Multispectral image pansharpening is a significant technology for remote sensing image analysis, aiming to restore a high-resolution multispectral (HRMS) image by merging a high-resolution panchromatic (PAN) image with a low-resolution multispectral (MS) image. While the convolutional neural networks (CNN) have garnered considerable attention for t...
Multi-exposure image fusion (MEF) provides a simple, effective, and low-cost solution to fill the gap between the high dynamic range of natural scenes and the low dynamic range of commonly used imaging sensors. In recent years, deep learning has made remarkable advances in MEF. However, it remains relatively challenging to make a fused image retain...
In this letter, to better supplement the advantages of features at different levels and improve the feature extraction ability of the network, a novel multi-level feature interaction transformer network (MFITN) is proposed for pansharpening, aiming to fuse multispectral (MS) and panchromatic (PAN) images. In MFITN, a multi-level feature interaction...
Pansharpening is a process that fuses a multispectral (MS) image with a panchromatic (PAN) image to generate a high-resolution multispectral (HRMS) image. Current methods often overlook scale inconsistency and the correlation within and between a window domain, resulting in suboptimal outcomes. Additionally, the use of deep CNN or Transformer often...
Pansharpening is to fuse a panchromatic (PAN) image with a multispectral (MS) image to obtain a high-spatial resolution multispectral (HRMS) image. Although the denoising diffusion probabilistic model can generate high-quality image details, its inherent stochasticity can lead to spectral and spatial distortions in the pansharpening task, and the a...
Pansharpening aims to obtain a high-spatial-resolution multispectral (MS) image by fusing a lower-spatial resolution MS image with a high-spatial-resolution panchromatic (PAN) image. Currently, the results obtained by most pansharpening methods still suffer from spatial and spectral distortion issues. The diffusion model has shown outstanding perfo...
Owing to insufficient light, images captured in low-light environment have a series of image degradation problems such as low visibility, color deviation, and noise. To address these problems, an image enhancement network based on multi-scale residual feature integration (IEN-MRFI) is proposed, which includes two modules: a shallow feature extracti...
Images captured in low-light environments suffer from severe degradation, which can be unfavorable for human observation and subsequent computer vision tasks. Although many enhancement methods based on deep learning have been proposed, the obtained enhancement images still suffer from drawbacks such as color distortion, noise, and blur. To solve th...
Due to the lack of reference images for the training of infrared and visible image fusion (IVIF) network, the deep learning models cannot fuse the modal features of different source images well, resulting in fusion results that are biased towards one modality. This study proposes an IVIF method based on a dual-supervised mask generation network (DS...
In recent years, deep convolution neural networks have made significant progress in single-image super-resolution (SISR). However, high-resolution (HR) images obtained by most SISR reconstruction methods still suffer from edge blur-ring and texture distortion. To address this issue, we propose a two-stage feature enhancement network (TFEN) for the...
Pansharpening is to fuse a panchromatic (PAN) image with a multispectral (MS) image to obtain a high-spatial-resolution multispectral (HRMS) image. The deep learning-based pansharpening methods usually apply the convolution operation to extract features and only consider the similarity of gradient information between PAN and HRMS images, resulting...
Although analyzing the brain's functional and structural network has revealed that numerous brain networks are necessary to collaborate during deception, the directionality of these functional networks is still unknown. This study investigated the effective connectivity of the brain networks during deception and uncovers the information-interaction...
Images captured in low-light environments have problems of insufficient brightness and low contrast, which will affect subsequent image processing tasks. Although most current enhancement methods can obtain high-contrast images, they still suffer from noise amplification and color distortion. To address these issues, this paper proposes a low-light...
Currently, single image super-resolution methods based on convolutional neural networks have achieved remarkable results. These methods typically increase the depth and complexity of a network to improve its performance, which increases the network’s computational burden. To solve these problems, this paper proposes a new lightweight bidirectional...
Multi-modal medical image fusion (MMIF) integrates medical images of different modalities into an image with rich information to boost the accuracy and efficiency of clinical diagnosis and treatment. There are two main problems in medical image fusion: 1) It is difficult to balance the computational efficiency and fusion quality; 2) in the clinic,...
Deep learning (DL)-based pansharpening methods have shown great advantages in extracting spectral–spatial features from multispectral (MS) and panchromatic (PAN) images compared with traditional methods. However, most DL-based methods ignore the local inner connection between the source images and the high-resolution MS (HRMS) image, which cannot f...
Deep learning has been widely used in infrared and visible image fusion owing to its strong feature extraction and generalization capabilities. However, it is difficult to directly extract specific image features from different modal images. Therefore, according to the characteristics of infrared and visible images, this paper proposes a multi-atte...
Pansharpening is a process of fusing a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to obtain a high-resolution multispectral (HRMS) image. Convolutional neural networks (CNNs) have been commonly utilized in this field because of their remarkable learning capabilities. However, their convolutional operat...
Image dehazing is of great importance and has been widely studied, as haze severely affects many high-level computer vision tasks. In this paper, by considering the gradual dissipation process of haze, a progressive dehazing network (PDN) is proposed. The proposed approach realizes haze removal step by step by constructing two main modules: the pre...
High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption. To achieve high video security, to the best of our knowledge, almost all of the existing HEVC encryption algorithms mainly encrypt the whole video, such that the user without permissions cannot obtain any viewable informa...
Face sketch-to-photo transformation aims at generates face photo images from sketched face images. Although transformations have progressed significantly with the development of deep learning techniques in recent years, generating face photos with realistic photo styles and rich facial details is still challenging. In this paper, a new realistic fa...
The automated segmentation of retinal blood vessels plays an important role in the computer aided diagnosis of retinal diseases. In this study, we propose a novel retinal vessel segmentation method based on residual attention and dual-supervision cascaded U-Net (RADCU-Net). Specifically, a residual attention U-Net (RAU-Net), including a residual un...
Images taken on rainy days often lose a significant amount of detailed information owing to the coverage of rain streaks, which interfere with the recognition and detection of the intelligent vision systems. It is, therefore, extremely important to recover clean rain-free images from the rain images. In this paper, we propose a rain removal method...
Images captured under low-light conditions often suffer from severe loss of structural details and color; therefore, image-enhancement algorithms are widely used in low-light image restoration. Image-enhancement algorithms based on the traditional Retinex model only consider the change in the image brightness, while ignoring the noise and color dev...
Pansharpening techniques fuse the complementary information from panchromatic (PAN) and multispectral (MS) images to obtain a high-resolution MS image. However, the majority of existing pansharpening techniques suffer from spectral distortion owing to the low correlation between the MS and PAN images, and difficulties in obtaining appropriate injec...
Multiexposure image fusion (MEF) is used to generate a high-quality image from a series of images with different exposure levels. The multiscale-based MEF method achieves better fusion performance than the single-scale-based method because it can better preserve the global contrast information. However, this method still has the problem of the loss...
Pansharpening is a technology involving information integration and processing in remote sensing imagery. It is applied to generate a high-resolution multispectral (HRMS) image through an effective fusion of a low spatial resolution multispectral image and a panchromatic (PAN) image. In this article, we propose an end-to-end multiscale and multidis...
To reduce the loss of detail and spectral information during the network propagation and better extract detail and spectral features at different scales, a novel dual-information compensation network for pansharpening (DCNP) is proposed for fusing multispectral (MS) and panchromatic (PAN) images. In the network, the domain-specific knowledge is con...
Deep-learning-based pansharpening methods have achieved remarkable results due to their powerful feature representation ability. However, the existing deep-learning-based pansharpening methods not only lack information exchange and sharing between features of different resolutions but also cannot effectively use the residual information at differen...
The purpose of pansharpening is to fuse a multispectral (MS) image with a panchromatic (PAN) image to generate a high spatial-resolution multispectral (HRMS) image. However, the traditional pansharpening methods do not adequately take consideration of the information of MS images, resulting in inaccurate detail injection and spectral distortion in...
Pansharpening is used to fuse a panchromatic (PAN) image with a multispectral (MS) image to obtain a high-spatial-resolution multispectral (HRMS) image. Traditional pansharpening methods face difficulties in obtaining accurate details and have low computational efficiency. In this study, a unified pansharpening model based on the band-adaptive grad...
The fusion of infrared and visible images combines the advantages of thermal radiation information in infrared images and texture information in visible images. To preserve salient targets and obtain better fusion results, this paper proposes a novel infrared and visible image fusion method based on dual-kernel side window filtering and detail opti...
For multi-sensor fusion of infrared and visible images, it is difficult to retain the thermal radiation information of the infrared image and the texture information of the visible image in the fused image. To overcome this problem, a novel infrared and visible image fusion method based on a modified side window filter (MSWF) and an intensity trans...
A VGG-based fusion method (named VMDM-Fusion) that employs multiple decision maps is proposed to fuse infrared and visible images. Our method first feeds the infrared and visible images into a pre-trained model of VGG-16 to extract the features. Then, a feature representation method we designed uses these features to construct saliency maps. Next,...
Pansharpening aims at fusing a multispectral (MS) image and panchromatic (PAN) image to obtain a high spatial resolution multispectral (HRMS) image. To obtain accurate details and reduce spectral distortion, this letter proposes an efficient pansharpening approach based on texture correction (TC) and detail refinement. First, a TC model is construc...
Methods of rain removal based on deep learning have rapidly developed, and the image quality after rain removal is continuously improving. However, the results of most methods have some common problems, including a loss of details, a blurring of edges, and the existence of artifacts. To remove rain-related information more thoroughly and retain mor...
This paper proposes an effective infrared and visible image fusion method based on a texture conditional generative adversarial network (TC-GAN). The constructed TC-GAN generates a combined texture map for capturing gradient changes in image fusion. The generator in the TC-GAN is designed as a codec structure for extracting more details, and a sque...
Face sketch synthesis aims to generate a face sketch image from a corresponding photo image and has wide applications in law enforcement and digital entertainment. Despite the remarkable achievements that have been made in face sketch synthesis, most existing works pay main attention to the facial content transfer, at the expense of facial detail i...
Multi-modal medical image fusion (MMIF) technology can effectively improve the efficiency and accuracy of doctors in clinical diagnosis and treatment by combining different modes of medical images into a medical image with rich information. To achieve an optimal balance between computational loss and fusion quality, this paper presents a new MMIF m...
Pansharpening fuses a low spatial resolution multi-spectral (MS) image with the corresponding panchromatic (PAN) image to obtain a high spatial resolution MS (HRMS) image. Traditional fusion methods may easily cause a spectral or spatial distortion when injecting details into an MS image. To preserve the spectral and spatial information, an efficie...
High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption. To achieve high video security, to the best of our knowledge, almost all of the existing HEVC encryption algorithms mainly encrypt the whole video, such that the user without permissions cannot obtain any viewable informa...
Remote sensing image fusion is to fuse low spatial resolution multispectral (MS) images with high spatial resolution panchromatic (PAN) images to get high spatial resolution multispectral images. The component substitute (CS)-based methods are popular approaches for their high efficiency and high spatial resolution. However, they may produce spectr...
Pansharpening is the process of fusing low spatial resolution multispectral (MS) images with high spatial resolution panchromatic (PAN) images, so as to obtain high spatial resolution multispectral (HRMS) images. In this study, a new pansharpening method based on a joint-guided detail extraction is proposed to maintain the spectral and spatial fide...
Image dehazing is a challenging task of restoring a clear image from a haze-polluted image. However, most popular dehazing methods have color shift and overexposure problems owing to the inaccurate estimation of the transmission and atmospheric light in the atmospheric scattering model. In view of the existing problems, this paper proposes a simple...