IEEE transactions on image processing : a publication of the IEEE Signal Processing Society最新文献

Tensor robust principal component analysis (TRPCA), as a popular linear low-rank method, has been widely applied to various visual tasks. The mathematical process of the low-rank prior is derived from the linear latent variable model. However, for nonlinear tensor data with rich information, their nonlinear structures may break through the assumption of low-rankness and lead to the large approximation error for TRPCA. Motivated by the latent low-dimensionality of nonlinear tensors, the general paradigm of the nonlinear tensor plus sparse tensor decomposition problem, called tensor robust kernel principal component analysis (TRKPCA), is first established in this paper. To efficiently tackle TRKPCA problem, two novel nonconvex regularizers the kernelized tensor Schatten-p norm (KTSPN) and generalized nonconvex regularization are designed, where the former KTSPN with tighter theoretical support adequately captures nonlinear features (i.e., implicit low-rankness) and the latter ensures the sparser structural coding, guaranteeing more robust separation results. Then by integrating their strengths, we propose a double nonconvex TRKPCA (DNTRKPCA) method to achieve our expectation. Finally, we develop an efficient optimization framework via the alternating direction multiplier method (ADMM) to implement the proposed nonconvex kernel method. Experimental results on synthetic data and several real databases show the higher competitiveness of our method compared with other state-of-the-art regularization methods. The code has been released in our ResearchGate homepage: https://www.researchgate.net/publication/397181729 DNTRKPCA code.

In recent years, with the development of autonomous driving, 3D reconstruction for unbounded large-scale scenes has attracted researchers' attention. Existing methods have achieved outstanding reconstruction accuracy in autonomous driving scenes, but most of them lack the ability to edit scenes. Although some methods have the capability to edit scenarios, they are highly dependent on manually annotated 3D bounding boxes, leading to their poor scalability. To address the issues, we introduce a new Gaussian representation, called DrivingEditor, which decouples the scene into two parts and handles them by separate branches to individually model the dynamic foreground objects and the static background during the training process. By proposing a framework for decoupled modeling of scenarios, we can achieve accurate editing of any dynamic target, such as dynamic objects removal, adding and etc, meanwhile improving the reconstruction quality of autonomous driving scenes especially the dynamic foreground objects, without resorting to 3D bounding boxes. Extensive experiments on Waymo Open Dataset and KITTI benchmarks demonstrate the performance in 3D reconstruction for both dynamic and static scenes. Besides, we conduct extra experiments on unstructured large-scale scenarios, which can more convincingly demonstrate the performance and robustness of our proposed model when rendering the unstructured scenes. Our code is available at https://github.com/WangXu-xxx/DrivingEditor.

In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitted to map a latent space to a degraded (e.g. noisy) image but in the process learns to reconstruct the clean image. This phenomenon is attributed to CNN's internal image prior. We revisit the DIP framework, examining it from the perspective of a neural implicit representation. Motivated by this perspective, we replace the random latent with Fourier-Features (Positional Encoding). We empirically demonstrate that the convolution layers in DIP can be replaced with simple pixel-level MLPs thanks to the Fourier features properties. We also prove that they are equivalent in the case of linear networks. We name our scheme "Positional Encoding Image Prior" (PIP) and exhibit that it performs very similar to DIP on various image-reconstruction tasks with much fewer parameters. Furthermore, we demonstrate that PIP can be easily extended to videos, an area where methods based on image-priors and certain INR approaches face challenges with stability. Code and additional examples for all tasks, including videos, are available on the project page nimrodshabtay.github.io/PIP.