Abstract Mesh denoising is a fundamental yet challenging task. Most of the existing data‐driven methods only consider the zero‐order information (vertex location) and first‐order information (face normal). However, higher‐order geometric information (such as curvature) is more descriptive for the shape of the mesh. Therefore, in order to impose such high‐order information, this paper proposes a novel Curvature‐Driven Multi‐Stream Graph Convolutional Neural Network (CDMS‐Net) architecture. CDMS‐Net has three streams, including curvature stream, face normal stream and vertex stream, where the curvature stream focuses on the high‐order Gaussian curvature information. Moreover, CDMS‐Net proposes a novel block based on residual dense connections, which is used as the core component to extract geometric features from meshes. This innovative design improves the performance of feature‐preserving denoising. The plug‐and‐play modular design makes CDMS‐Net easy to be implemented. Multiple sets of ablation study are carried out to verify the rationality of the CDMS‐Net. Our method establishes new state‐of‐the‐art mesh denoising results on publicly available datasets.

In this paper, we propose a novel algorithm for 3D mesh filtering (Structural Adaptive Filtering, SAF) based on mesh structural adaptation. As we all know, 3D meshes mainly have three types of geometric features: corners, edges, and planes. Therefore, we designed a protection mechanism for these three types of features to achieve the feature-preserving denoising. In the first step, for the faces normals to be processed, we build a variable set of similarity between the face normal and the neighborhood faces normal, calculate their coefficient of variation, variance, and quartile difference, and then select the neighborhood face normals with high similarity to update the current normals through self adaptation of these variables. In the second step, all vertices complete the iterative update of vertex coordinates according to the filtered face normals. Unlike existing 3D mesh denoising algorithms, which have too many parameters to manually set thresholds and are sensitive to parameters, SAF is based on the geometry of local faces (no need to manually set denoising thresholds). SAF only needs to set the iterative parameters to complete high-performance feature-preserving filtering, which has high practical value. We demonstrate through extensive experimental data that SAF outperforms or is comparable to state-of-the-art methods in feature-preserving denoising at different noise levels.

In conditions such as inclement weather or in-sufficient lighting at night, video captured by camera equipment may be insufficiently bright and have low contrast. With the popularity of ultra-high-definition video images, its ultra-high resolution makes the performance requirements of video processing algorithms more and more stringent. Aiming at these problems, this paper proposes a curvature-based real-time brightness adjustment algorithm for ultra-high-definition video. The algorithm is implemented in two steps: global brightness adaptive enhancement and local contrast adaptive enhancement. The experimental results show that the algorithm proposed in this paper not only highlights the details of low-brightness areas of video images, but also avoids excessive enhancement of high-brightness areas. It is superior or comparable to the comparison algorithm in both subjective visual effects and objective evaluation indicators. In addition, because the algorithm in this paper is simple and easy to implement, it can be easily implemented in parallel, and can be applied to real-time processing of ultra-high-definition video, which has high practical value.

Weighted Gaussian curvature is an important smoothness measurement for images. However, its conventional computation scheme has low performance, low accuracy and requires that the input image must be second order differentiable. To tackle these three issues, we propose a novel discrete computation scheme for the weighted Gaussian curvature. Our scheme does not require the second order differentiability. Moreover, our scheme is more accurate, has smaller support region and computationally more efficient than the conventional schemes. Therefore, our scheme holds promise for a large range of applications where the weighted Gaussian curvature is needed, for example, image smoothing, cartoon texture decomposition, optical flow estimation, etc.

This paper presents a quarter Laplacian filter that can preserve corners and edges during image smoothing. Its support region is $2\times 2$, which is smaller than the $3\times 3$ support region of the classical Laplacian filter. Thus, it is more local. Moreover, this filter can be implemented via the classical box filter, leading to high performance for real time applications. Finally, we show its edge preserving property in several image processing tasks, including image smoothing, texture enhancement, and low-light image enhancement. The proposed filter can be adopted in a wide range of image processing applications.

Despite excellent progress in recent years, mode collapse remains a major unsolved problem in generative adversarial networks (GANs). In this paper, we present spectral regularization for GANs (SR-GANs), a new and robust method for combating the mode collapse problem in GANs. Theoretical analysis shows that the optimal solution to the discriminator has a strong relationship to the spectral distributions of the weight matrix. Therefore, we monitor the spectral distribution in the discriminator of spectral normalized GANs (SN-GANs), and discover a phenomenon which we refer to as spectral collapse, where a large number of singular values of the weight matrices drop dramatically when mode collapse occurs. We show that there are strong evidence linking mode collapse to spectral collapse; and based on this link, we set out to tackle spectral collapse as a surrogate of mode collapse. We have developed a spectral regularization method where we compensate the spectral distributions of the weight matrices to prevent them from collapsing, which in turn successfully prevents mode collapse in GANs. We provide theoretical explanations for why SR-GANs are more stable and can provide better performances than SN-GANs. We also present extensive experimental results and analysis to show that SR-GANs not only always outperform SN-GANs but also always succeed in combating mode collapse where SN-GANs fail.

Hyperspectral (HS) and multispectral (MS) image fusion has attracted great attention during the past decades. Numerous of fusion methods have been developed and shown their effectiveness particularly on simulated data. Nonetheless, for real remote sensing data, the different acquisition times or conditions result in a serious spectral distortion and severely affect the fusion quality. Yet very few works have considered this issue. In this paper, a spectral modulation (SM) method is proposed to better maintain the spectral information of the HS data when fusing with MS data. The goal is to generate an adjusted MS image that would have been observed under the same imaging conditions with the corresponding HS sensor. Experiments on two HS and MS data sets acquired by different platforms demonstrate that the proposed method is beneficial to the spectral fidelity and spatial enhancement of the fused image compared with some state-of-the-art fusion techniques.

This paper develops a multi-dimensional geometric features extraction method for monocular Simultaneous Localization and Mapping(SLAM), in order to meet the self-navigation need for light- weighted robots, e.g. small drones. The monocular SLAM mapping based on feature points method is vulnerable to noisy samples, and thus must be enhanced for better efficiency in complex environments. Our proposed method introduces the line and plane features to the three-dimensional map building process, so as to improve the speed of monocular SLAM applications system's key frames matching and overall stability. In particular, we develop a rapid line matching algorithm, where three-dimensional lines were drawn by two- dimensional lines matching. Numerical results show that our approach can significantly reduce redundant information in the SLAM applications.