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Found 12 papers, 1 papers with code
MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images
We improve from NeRF and propose a novel framework that, by leveraging the parametric 3DMM models, can reconstruct a high-fidelity drivable face avatar and successfully handle the unseen expressions.
WT-MVSNet: Window-based Transformers for Multi-view Stereo
In this work, we propose Window-based Transformers (WT) for local feature matching and global feature aggregation in multi-view stereo.
Adversarial Learning of Hard Positives for Place Recognition
Our method learns local and global augmentation policies which will increase the training loss, while the image retrieval network is forced to learn more powerful features for discriminating increasingly difficult examples.
ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching
Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching.
Controllable Continuous Gaze Redirection
The encoder maps images into a well-disentangled and hierarchically-organized latent space.
Learning Generic Diffusion Processes for Image Restoration
On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained.
Speckle Reduction with Trained Nonlinear Diffusion Filtering
Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality.
Learning Non-local Image Diffusion for Image Denoising
In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising.
Image Denoising via Multi-scale Nonlinear Diffusion Models
However, in recent two years, discriminatively trained local approaches have started to outperform previous non-local models and have been attracting increasing attentions due to the additional advantage of computational efficiency.
Poisson Noise Reduction with Higher-order Natural Image Prior Model
We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR.
Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion
The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision and microscopy.
A higher-order MRF based variational model for multiplicative noise reduction
The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems.
