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学者姓名：施云惠
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Abstract ：
Convolutional neural networks (CNN) have achieved remarkable results in various computer vision and pattern recognition applications. However, in computer graphics and geometry processing, the focus is on non-Euclidean structured meshed surfaces. Since CNNs operate based on Euclidean domains, the fundamental operations of CNNs, such as convolution and pooling, are not well defined in non-Euclidean domains. To address this issue, we propose a novel mesh representation named Heat Kernel Mesh (HKM), which utilizes the heat diffusion on the non-Euclidean domain. The HKM represents a meshed surface as a spatio-temporal graph signal, sampled on the edges of the mesh at each time interval with a Euclidean-like structure. Furthermore, we propose the Heat Kernel Mesh-Based Convolutional Neural Network (HKMCNN), where convolution, pooling, and attention mechanism are designed based on the property of our representation and operate on edges. For the fine-grained classification, we propose distance Heat Kernel Mesh (dHKM) that can identify discriminant features with the HKMCNN to represent a mesh. Extensive experiments on mesh classification and segmentation demonstrate the effectiveness and efficiency of the proposed HKMCNN.
Keyword ：
CNN CNN 3D representation 3D representation Segmentation Segmentation Heat diffusion Heat diffusion Classification Classification Mesh Mesh
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| GB/T 7714 | Li, Tingting , Shi, Yunhui , Gao, Junbin et al. HKMCNN: Heat Kernel Mesh-Based Convolutional Neural Networks [J]. | KNOWLEDGE-BASED SYSTEMS , 2025 , 317 . |
| MLA | Li, Tingting et al. "HKMCNN: Heat Kernel Mesh-Based Convolutional Neural Networks" . | KNOWLEDGE-BASED SYSTEMS 317 (2025) . |
| APA | Li, Tingting , Shi, Yunhui , Gao, Junbin , Wang, Jin , Yin, Baocai . HKMCNN: Heat Kernel Mesh-Based Convolutional Neural Networks . | KNOWLEDGE-BASED SYSTEMS , 2025 , 317 . |
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Abstract ：
Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: (1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features; and (2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet sub-bands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale sub-bands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet sub-band based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important sub-bands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method.
Keyword ：
wavelet tree wavelet tree Compressed sensing Compressed sensing deep unfolding deep unfolding
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| GB/T 7714 | Han, Kai , Wang, Jin , Shi, Yunhui et al. WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing [J]. | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2025 , 21 (1) . |
| MLA | Han, Kai et al. "WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing" . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 21 . 1 (2025) . |
| APA | Han, Kai , Wang, Jin , Shi, Yunhui , Cai, Hanqin , Ling, Nam , Yin, Baocai . WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing . | ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS , 2025 , 21 (1) . |
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Abstract ：
Recently, deep unfolding network methods have significantly progressed in hyperspectral snapshot compressive imaging. Many approaches directly employ Transformer models to boost the feature representation capabilities of algorithms. However, they often fall short of leveraging the full potential of self-attention mechanisms. Additionally, current methods lack adequate consideration of both intra-stage and inter-stage feature fusion, which hampers their overall performance. To tackle these challenges, we introduce a novel approach that hybridizes the sparse Transformer and wavelet fusion-based deep unfolding network for hyperspectral image (HSI) reconstruction. Our method includes the development of a spatial sparse Transformer and a spectral sparse Transformer, designed to capture spatial and spectral attention of HSI data, respectively, thus enhancing the Transformer's feature representation capabilities. Furthermore, we incorporate wavelet-based methods for both intra-stage and inter-stage feature fusion, which significantly boosts the algorithm's reconstruction performance. Extensive experiments across various datasets confirm the superiority of our proposed approach.
Keyword ：
hyperspectral image reconstruction hyperspectral image reconstruction compressive sensing compressive sensing snapshot compressive imaging snapshot compressive imaging deep unfolding network deep unfolding network
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| GB/T 7714 | Ying, Yangke , Wang, Jin , Shi, Yunhui et al. Hybrid Sparse Transformer and Wavelet Fusion-Based Deep Unfolding Network for Hyperspectral Snapshot Compressive Imaging [J]. | SENSORS , 2024 , 24 (19) . |
| MLA | Ying, Yangke et al. "Hybrid Sparse Transformer and Wavelet Fusion-Based Deep Unfolding Network for Hyperspectral Snapshot Compressive Imaging" . | SENSORS 24 . 19 (2024) . |
| APA | Ying, Yangke , Wang, Jin , Shi, Yunhui , Ling, Nam . Hybrid Sparse Transformer and Wavelet Fusion-Based Deep Unfolding Network for Hyperspectral Snapshot Compressive Imaging . | SENSORS , 2024 , 24 (19) . |
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Abstract ：
The surge in image data has significantly increased the pressure on storage and transmission, posing new challenges for image compression technology. The structural texture of an image implies its statistical characteristics, which is effective for image encoding and decoding. Consequently, content-adaptive compression methods based on learning can better capture the content attributes of images, thereby enhancing encoding performance. However, learned image compression methods do not comprehensively account for both the global and local correlations among the pixels within an image. Moreover, they are constrained by rate-distortion optimization, which prevents the attainment of a compact representation of image attributes. To address these issues, we propose a syntax-guided content-adaptive transform framework that efficiently captures image attributes and enhances encoding efficiency. Firstly, we propose a syntax-refined side information module that fully leverages syntax and side information to guide the adaptive transformation of image attributes. Moreover, to more thoroughly exploit the global and local correlations in image space, we designed global-local modules, local-global modules, and upsampling/downsampling modules in codecs, further eliminating local and global redundancies. The experimental findings indicate that our proposed syntax-guided content-adaptive image compression model successfully adapts to the diverse complexities of different images, which enhances the efficiency of image compression. Concurrently, the method proposed has demonstrated outstanding performance across three benchmark datasets.
Keyword ：
image compression image compression deep learning deep learning adaptive compression adaptive compression
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| GB/T 7714 | Shi, Yunhui , Ye, Liping , Wang, Jin et al. Syntax-Guided Content-Adaptive Transform for Image Compression [J]. | SENSORS , 2024 , 24 (16) . |
| MLA | Shi, Yunhui et al. "Syntax-Guided Content-Adaptive Transform for Image Compression" . | SENSORS 24 . 16 (2024) . |
| APA | Shi, Yunhui , Ye, Liping , Wang, Jin , Wang, Lilong , Hu, Hui , Yin, Baocai et al. Syntax-Guided Content-Adaptive Transform for Image Compression . | SENSORS , 2024 , 24 (16) . |
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Abstract ：
基于窗口注意力的神经数据依赖变换的图像压缩方法属于计算机视觉领域。考虑每个输入图像的率失真RD性能很有意义，现有方法没有充分考虑每个图像的概率属性和局部纹理，RD性能有待进一步提高。本发明中扩展的窗口注意力模型(EWAM)联合学习图像的概率属性和局部纹理。一种基于卷积神经网络的框架，包括以下组件：语法生成器和权重生成器，通过模型流来学习语法和语法权重；上下文模型，通过内容流来学习内容；超先验模型，通过潜在表示学习分布；以及EWAM，通过窗口注意力进一步提高概率属性的精度和局部纹理的清晰度。本发明不仅能够在线优化每张图像的RD性能，而且具有更清晰的纹理和结构，在客观指标上优于目前最先进的方法。
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| GB/T 7714 | 施云惠 , 叶莉萍 , 王瑾 et al. 基于窗口注意力的神经数据依赖变换的图像压缩方法 : CN202310580643.7[P]. | 2023-05-23 . |
| MLA | 施云惠 et al. "基于窗口注意力的神经数据依赖变换的图像压缩方法" : CN202310580643.7. | 2023-05-23 . |
| APA | 施云惠 , 叶莉萍 , 王瑾 , 尹宝才 . 基于窗口注意力的神经数据依赖变换的图像压缩方法 : CN202310580643.7. | 2023-05-23 . |
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Abstract ：
一种基于超先验可变码率图像压缩的精准R‑λ模型码率控制方法属于计算机视觉领域，针对基于超先验可变码率图像压缩框架无法对于单张图像的目标码率给出相应的拉格朗日乘子。本方法为任意图像和任意基于超先验可变码率图像压缩模型建立拉格朗日乘子λ和码率R的关系，提出精准R‑λ模型。通过3次编码拟合单张图像的精准R‑λ模型。计算出对应目标码率应该输入的拉格朗日乘子。最后对于该图像的任意输入码率，都可以输出与输入码率相近的输出码率，平均误差控制在0.6％下，实现精准的码率控制。
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| GB/T 7714 | 施云惠 , 王鹏权 , 王瑾 et al. 一种基于超先验可变码率图像压缩的精准R-λ模型码率控制方法 : CN202310579557.4[P]. | 2023-05-23 . |
| MLA | 施云惠 et al. "一种基于超先验可变码率图像压缩的精准R-λ模型码率控制方法" : CN202310579557.4. | 2023-05-23 . |
| APA | 施云惠 , 王鹏权 , 王瑾 , 尹宝才 . 一种基于超先验可变码率图像压缩的精准R-λ模型码率控制方法 : CN202310579557.4. | 2023-05-23 . |
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Abstract ：
本发明为了解决球面图像压缩效率问题，提出了一种基于球面小波变换的球面图像压缩方法，包括采用现有的基于球面测度的球面图像表示SMSIR对球面三角像元进行索引，利用基于SMSIR的球面小波变换对球面三角像元进行变换，利用SMSIR图像压缩方案S‑SPIHT对变换后的球面小波系数进行扫描编解码完成球面图像压缩，所述的S‑SPIHT是对SPIHT的改进，改进之处在于像素坐标的设置，即球形图像使用三维坐标dk(pk, qk, mk)表示，除此之外改进之处还可以是同时改变像素坐标的设置以及重新设计SPIHT的扫描顺序，具体包括有序根树索引扫描ORTIS、二进索引逐行扫描DIPS、以及二进索引交叉扫描DICS。
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| GB/T 7714 | 施云惠 , 王欢 , 王瑾 et al. 基于球面小波变换的球面图像压缩方法 : CN202210019249.1[P]. | 2022-01-07 . |
| MLA | 施云惠 et al. "基于球面小波变换的球面图像压缩方法" : CN202210019249.1. | 2022-01-07 . |
| APA | 施云惠 , 王欢 , 王瑾 , 吴刚 , 尹宝才 . 基于球面小波变换的球面图像压缩方法 : CN202210019249.1. | 2022-01-07 . |
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Abstract ：
本发明涉及一种基于学习的边信息补偿图像压缩方法，用于解决图像和视频的压缩问题，具体包括本发明通过引入多尺度表示提取两层边信息作为浅层超先验和深层超先验，从而实现更准确和灵活的熵模型。此外，浅层超先验可以捕获潜在表示的空间依赖，同时也可以微调潜在表示来提升重建质量。其次，本发明提取的深层超先验作为浅层超先验的超先验，可以提升浅层超先验的有效性和准确性。最后，本发明设计了一种有效的残差通道注意力块，可以增强潜在表示通道之间的交互关系以及适用于我们基于残差的网络结构。
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| GB/T 7714 | 施云惠 , 张康富 , 王瑾 et al. 一种基于学习的超先验边信息补偿图像压缩方法 : CN202210011926.5[P]. | 2022-01-06 . |
| MLA | 施云惠 et al. "一种基于学习的超先验边信息补偿图像压缩方法" : CN202210011926.5. | 2022-01-06 . |
| APA | 施云惠 , 张康富 , 王瑾 , 尹宝才 . 一种基于学习的超先验边信息补偿图像压缩方法 : CN202210011926.5. | 2022-01-06 . |
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Abstract ：
一种基于结构和纹理合成的概念图像压缩方法属于计算机视觉领域，本发明所提的压缩框架允许将图像编码为紧凑的、高级可解释的表示来进行重建。利用图像处理技术，将原图转换为保边平滑的结构图，得到图像的结构层，提供图像的结构信息；从原图中心提取的纹理块作为图像的纹理层，提供图像的局部详细纹理信息。将图像的结构层和纹理层进行下采样，结合BPG编码器实现了图像的极致压缩，并用超分辨率模型进行上采样与生成模型保证重建质量。
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| GB/T 7714 | 施云惠 , 赵新丽 , 王瑾 et al. 一种基于结构和纹理合成的概念图像压缩方法 : CN202211530532.7[P]. | 2022-12-01 . |
| MLA | 施云惠 et al. "一种基于结构和纹理合成的概念图像压缩方法" : CN202211530532.7. | 2022-12-01 . |
| APA | 施云惠 , 赵新丽 , 王瑾 , 尹宝才 . 一种基于结构和纹理合成的概念图像压缩方法 : CN202211530532.7. | 2022-12-01 . |
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Abstract ：
三维网格表示方法及三维网格卷积神经网络模型，能够相对完整地保留形状内在几何结构信息，在特征提取方面优于现有的方法，在应用于3D网格的各种学习任务(如分类和分割)中获得了更高的准确率。该方法针对三维形状图像数据，通过对网格表面的连续热信号进行重采样而得到的边信号。
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| GB/T 7714 | 施云惠 , 李婷婷 , 王瑾 et al. 三维网格表示方法及其卷积神经网络模型 : CN202210513680.1[P]. | 2022-05-11 . |
| MLA | 施云惠 et al. "三维网格表示方法及其卷积神经网络模型" : CN202210513680.1. | 2022-05-11 . |
| APA | 施云惠 , 李婷婷 , 王瑾 , 尹宝才 . 三维网格表示方法及其卷积神经网络模型 : CN202210513680.1. | 2022-05-11 . |
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