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| Adaptive Effective Wiener Filter- and Regression-Based | upsampling | for Asymmetric Resolution Stereoscopic Video Coding |
| Angular | upsampling | of projection measurements in 3D computed tomography using a sparsity prior |
| APUNet: Attention-guided | upsampling | network for sparse and non-uniform point cloud |
| Arbitrary Point Cloud | upsampling | Via Dual Back-Projection Network |
| ASUR3D: Arbitrary Scale | upsampling | and Refinement of 3D Point Clouds using Local Occupancy Fields |
| Bi-RSTU: Bidirectional Recurrent | upsampling | Network for Space-Time Video Super-Resolution |
| Bilateral | upsampling | Network for Single Image Super-Resolution With Arbitrary Scaling Factors |
| BLAST-NET: Semantic Segmentation of Human Blastocyst Components via Cascaded Atrous Pyramid and Dense Progressive | upsampling | |
| Color Kernel Regression for Robust Direct | upsampling | from Raw Data of General Color Filter Array |
| Color-guided boundary-preserving depth | upsampling | based on L0 gradient minimization |
| Consensus-Driven Approach for Structure and Texture Aware Depth Map | upsampling | , A |
| CUF: Continuous | upsampling | Filters |
| Deep Fashion Analysis with Feature Map | upsampling | and Landmark-Driven Attention |
| Deep Learning-Based Point | upsampling | for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization |
| Deep Magnification-Flexible | upsampling | Over 3D Point Clouds |
| Deep Space-time Video | upsampling | Networks |
| Deep Video Super-Resolution Network Using Dynamic | upsampling | Filters Without Explicit Motion Compensation |
| Depth image | upsampling | based on guided filter with low gradient minimization |
| Depth map super-resolution via iterative joint-trilateral- | upsampling | |
| Depth map | upsampling | by self-guided residual interpolation |
| Depth Map | upsampling | via Compressive Sensing |
| Depth map | upsampling | with a confidence-based joint guided filter |
| Depth | upsampling | based on deep edge-aware learning |
| Depth | upsampling | by depth prediction |
| Depth | upsampling | method via Markov random fields without edge-misaligned artifacts |
| Devil is in the | upsampling | : Architectural Decisions Made Simpler for Denoising with Deep Image Prior, The |
| Diffusion Tensor Images | upsampling | : A Registration-Based Approach |
| Directional Filtering for | upsampling | According to Direction Information of the Base Layer in the JVT/SVC Codec |
| Directional Super-Resolution by Means of Coded Sampling and Guided | upsampling | |
| Downsampling dependent | upsampling | of images |
| Edge-Preserving Depth Map | upsampling | by Joint Trilateral Filter |
| Efficient Light Field Angular Super-Resolution With Sub-Aperture Feature Learning and Macro-Pixel | upsampling | |
| Efficient Space-time Video Super Resolution using Low-Resolution Flow and Mask | upsampling | |
| Enhancement of dynamic depth scenes by | upsampling | for precise super-resolution (UP-SR) |
| Enhancing view synthesis with image and depth map | upsampling | |
| Extended Guided Filtering for Depth Map | upsampling | |
| FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic | upsampling | |
| Fast edge-filtered image | upsampling | |
| Fast Exact Area Image | upsampling | with Natural Biquadratic Histosplines |
| Fast gradient-aware | upsampling | for cartoon video |
| Fast Image | upsampling | via the Displacement Field |
| Fast Scheme for Downsampling and | upsampling | in the DCT Domain, A |
| Fast single-image | upsampling | with relative edge growth rate priors |
| Fast video interpolation/ | upsampling | using linear motion model |
| Feature-Guided Spatial Attention | upsampling | for Real-Time Stereo Matching Network |
| Frame Rate Fusion and | upsampling | of EO/LIDAR Data for Multiple Platforms |
| Frequency-Selective Geometry | upsampling | of Point Clouds |
| Fusion of Median and Bilateral Filtering for Range Image | upsampling | |
| Gaussian Process | upsampling | Model for Improvements in Optical Character Recognition, A |
| Grad-PU: Arbitrary-Scale Point Cloud | upsampling | via Gradient Descent with Learned Distance Functions |
| Guidance-based improved depth | upsampling | with better initial estimate |
| Guided Depth | upsampling | via a Cosparse Analysis Model |
| Guided image | upsampling | using bitmap tracing |
| High quality depth map estimation by kinect | upsampling | and hole filling using RGB features and mutual information |
| High quality depth map | upsampling | for 3D-TOF cameras |
| High Resolution Local Structure-Constrained Image | upsampling | |
| High-Quality Depth Map | upsampling | and Completion for RGB-D Cameras |
| High-resolution Image Inpainting with Iterative Confidence Feedback and Guided | upsampling | |
| Hybrid parametric-nonparametric modeling with application to natural image | upsampling | |
| Image Guided Depth Map | upsampling | using Anisotropic TV-L2 |
| Image Guided Depth | upsampling | Using Anisotropic Total Generalized Variation |
| Image | upsampling | via texture hallucination |
| Image-guided depth map | upsampling | using normalized cuts-based segmentation and smoothness priors |
| Image-guided ToF depth | upsampling | : a survey |
| Improved | upsampling | filter design for spatially scalable video coding |
| Improving sub-pixel correspondence through | upsampling | |
| Intensity-Guided Depth | upsampling | Using Edge Sparsity and Weighted L_0 Gradient Minimization |
| Intensity-guided edge-preserving depth | upsampling | through weighted L0 gradient minimization |
| Investigating 3D holoscopic visual content | upsampling | using super-resolution for cultural heritage digitization |
| Joint bilateral propagation | upsampling | for unstructured multi-view stereo |
| Joint Chroma Downsampling and | upsampling | for Screen Content Image |
| Joint Geodesic | upsampling | of Depth Images |
| Joint Object Segmentation and Depth | upsampling | |
| Joint | upsampling | of random color distance maps for fast salient region detection |
| Joint-adaptive bilateral depth map | upsampling | |
| JUMPS: Joints | upsampling | Method for Pose Sequences |
| Learnable | upsampling | -Based Point Cloud Semantic Segmentation |
| Learning Affinity-Aware | upsampling | for Deep Image Matting* |
| Local and Global GANs With Semantic-Aware | upsampling | for Image Generation |
| low-complexity | upsampling | technique for H.264, A |
| majorize-minimize approach for high-quality depth | upsampling | , A |
| Meet-in-the-middle: Multi-scale | upsampling | and matching for cross-resolution face recognition |
| MobiUP: An | upsampling | -Based System Architecture for High-Quality Video Streaming on Mobile Devices |
| MRF-Based Depth | upsampling | : Upsample the Depth Map With Its Own Property, An |
| MRF-Based Disparity | upsampling | Using Stereo Confidence Evaluations |
| MRI | upsampling | Using Feature-Based Nonlocal Means Approach |
| Multilevel Modified Finite Radon Transform Network for Image | upsampling | |
| Multispectral demosaicking using adaptive kernel | upsampling | |
| Neural Points: Point Cloud Representation with Neural Fields for Arbitrary | upsampling | |
| new | upsampling | method for mobile LiDAR data, A |
| Noise-Aware Filter for Real-Time Depth | upsampling | , A |
| Noising-Denoising Framework for Point Cloud | upsampling | via Normalizing Flows, A |
| Nonlinear Image | upsampling | Method Based on Radial Basis Function Interpolation |
| NoUCSR: Efficient Super-Resolution Network without | upsampling | Convolution |
| OPE-SR: Orthogonal Position Encoding for Designing a Parameter-free | upsampling | Module in Arbitrary-scale Image Super-Resolution |
| Patch-Based Progressive 3D Point Set | upsampling | |
| Point Cloud | upsampling | via a Coarse-to-Fine Network |
| Point Cloud | upsampling | via Cascaded Refinement Network |
| Point Cloud | upsampling | via Disentangled Refinement |
| Point Cloud | upsampling | via Perturbation Learning |
| Point Cloud | upsampling | with Dynamic Graph Scattering Transform |
| Precise depth map | upsampling | and enhancement based on edge-preserving fusion filters |
| Pro-PULSE: Learning Progressive Encoders of Latent Semantics in GANs for Photo | upsampling | |
| Probability-based Global Cross-modal | upsampling | for Pansharpening |
| Progressive Point Cloud | upsampling | via Differentiable Rendering |
| PU-CTG: A Point Cloud | upsampling | Network Using Transformer Fusion and GRU Correction |
| PU-Dense: Sparse Tensor-Based Point Cloud Geometry | upsampling | |
| PU-EVA: An Edge-Vector based Approximation Solution for Flexible-scale Point Cloud | upsampling | |
| PU-GACNet: Graph Attention Convolution Network for Point Cloud | upsampling | |
| PU-GAN: A Point Cloud | upsampling | Adversarial Network |
| PU-GCN: Point Cloud | upsampling | using Graph Convolutional Networks |
| PU-Net: Point Cloud | upsampling | Network |
| PU-Transformer: Point Cloud | upsampling | Transformer |
| PU-WGCN: Point Cloud | upsampling | Using Weighted Graph Convolutional Networks |
| PUFA-GAN: A Frequency-Aware Generative Adversarial Network for 3D Point Cloud | upsampling | |
| Pugeo-net: A Geometry-centric Network for 3d Point Cloud | upsampling | |
| PULSE: Self-Supervised Photo | upsampling | via Latent Space Exploration of Generative Models |
| Quality-Efficient | upsampling | Method for Asymmetric Resolution Stereoscopic Video Coding With Interview Motion Compensation and Error Compensation |
| Quaternionic | upsampling | : Hyperspherical Techniques for 6 DoF Pose Tracking |
| Rethinking Multi-Contrast MRI Super-Resolution: Rectangle-Window Cross-Attention Transformer and Arbitrary-Scale | upsampling | |
| Reverse | upsampling | method and system |
| RGB-Guided Hyperspectral Image | upsampling | |
| Robust weighted least squares for guided depth | upsampling | |
| S3U-PVNet: Arbitrary-scale point cloud | upsampling | via Point-Voxel Network based on Siamese Self-Supervised Learning |
| Salient object detection via hybrid | upsampling | and hybrid loss computing |
| SAUM: Symmetry-aware | upsampling | Module for Consistent Point Cloud Completion |
| Scene flow estimation by depth map | upsampling | and layer assignment for camera-LiDAR system |
| Self-similarity matching with predictive linear | upsampling | for depth map |
| Self-Supervised Arbitrary-Scale Implicit Point Clouds | upsampling | |
| Self-Supervised Arbitrary-Scale Point Clouds | upsampling | via Implicit Neural Representation |
| Semantic Point Cloud | upsampling | |
| Semantically Guided Depth | upsampling | |
| Sequential Point Cloud | upsampling | by Exploiting Multi-Scale Temporal Dependency |
| SPU-Net: Self-Supervised Point Cloud | upsampling | by Coarse-to-Fine Reconstruction With Self-Projection Optimization |
| TasselNetV3: Explainable Plant Counting With Guided | upsampling | and Background Suppression |
| Texture-guided depth | upsampling | using Bregman split: A clustering graph-based approach |
| Thermal Face Recognition Based on Transformation by Residual U-net and Pixel Shuffle | upsampling | |
| TP-NoDe: Topology-aware Progressive Noising and Denoising of Point Clouds towards | upsampling | |
| Training-Free Mesh | upsampling | and Morphing Technique for 3D Face Rejuvuvenation, A |
| Unified Bayesian Approach to Multi-Frame Super-Resolution and Single-Image | upsampling | in Multi-Sensor Imaging, A |
| UPDCNN: A New Scheme for Image | upsampling | and Deblurring Using a Deep Convolutional Neural Network |
| UPFlow: | upsampling | Pyramid for Unsupervised Optical Flow Learning |
| upsampling | range data in dynamic environments |
| upsampling | the depth map with its own properties |
| Utilisation of edge adaptive | upsampling | in compression of depth map videos for enhanced free-viewpoint rendering |
| Variable Bandwidth Weighting for Texture Copy Artifact Suppression in Guided Depth | upsampling | |
| VPU: A Video-Based Point Cloud | upsampling | Framework |
| Weighted Chroma Downsampling and Luma-Referenced Chroma | upsampling | for HDR/WCG Video Coding |
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