Chen et al., 2023 - Google Patents
Depth-guided deep filtering network for efficient single image bokeh renderingChen et al., 2023
- Document ID
- 11868214764251738487
- Author
- Chen Q
- Zheng B
- Zhou X
- Huang A
- Sun Y
- Chen C
- Yan C
- Yuan S
- Publication year
- Publication venue
- Neural Computing and Applications
External Links
Snippet
Bokeh effect is usually used to highlight major contents in an image. Limited by the small sensors, cameras on smartphones are less sensitive to the depth information and cannot directly produce bokeh effect as pleasant as digital single lens reflex cameras. To address …
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20172—Image enhancement details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
- G06T5/001—Image restoration
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
- G06T3/40—Scaling the whole image or part thereof
- G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T1/00—General purpose image data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Kim et al. | Deformable kernel networks for joint image filtering | |
| Zhong et al. | High-resolution depth maps imaging via attention-based hierarchical multi-modal fusion | |
| Hu et al. | Depth-attentional features for single-image rain removal | |
| Wang et al. | End-to-end view synthesis for light field imaging with pseudo 4DCNN | |
| Yan et al. | Dual-attention-guided network for ghost-free high dynamic range imaging | |
| Won et al. | Learning depth from focus in the wild | |
| Dutta | Depth-aware blending of smoothed images for bokeh effect generation | |
| Zheng et al. | Constrained predictive filters for single image bokeh rendering | |
| Heber et al. | U-shaped Networks for Shape from Light Field. | |
| Guo et al. | Low-light image enhancement with joint illumination and noise data distribution transformation | |
| Liu et al. | Depth-guided learning light field angular super-resolution with edge-aware inpainting | |
| Kang et al. | Facial depth and normal estimation using single dual-pixel camera | |
| Yan et al. | Underwater image enhancement via multiscale disentanglement strategy | |
| Chudasama et al. | RSRGAN: computationally efficient real-world single image super-resolution using generative adversarial network | |
| Iwatsuki et al. | Unsupervised disparity estimation from light field using plug-and-play weighted warping loss | |
| Agrahari Baniya et al. | Frame selection using spatiotemporal dynamics and key features as input pre-processing for video super-resolution models | |
| Chen et al. | Depth-guided deep filtering network for efficient single image bokeh rendering | |
| Li et al. | Hybrid warping fusion for video frame interpolation | |
| Li et al. | Monocular human depth estimation with 3D motion flow and surface normals | |
| Catley-Chandar et al. | RoGUENeRF: a robust geometry-consistent universal enhancer for NeRF | |
| Dong et al. | Exploiting dual-correlation for multi-frame time-of-flight denoising | |
| US12288347B2 (en) | Method and apparatus with depth map generation | |
| Wang et al. | Light field depth estimation using occlusion-aware consistency analysis | |
| Kim et al. | Deformable kernel networks for guided depth map upsampling | |
| Sharma et al. | Multilevel progressive recursive dilated networks with correlation filter (MPRDNCF) for image super-resolution |