Hareesh et al., 2010 - Google Patents
A fast and simple gradient function guided filling order prioritization for exemplar-based color image inpaintingHareesh et al., 2010
- Document ID
- 2568662908291918406
- Author
- Hareesh A
- Chandrasekaran V
- Publication year
- Publication venue
- 2010 IEEE International Conference on Image Processing
External Links
Snippet
Image inpainting is the art of recovering the original image from images which are generally incomplete due to various factors, including degradation due to ageing, damage due to wear and tear and missing image details due to occlusion. In such situations, there is a need to …
- 238000000034 method 0 abstract description 8
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
- 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
- G06T11/00—2D [Two Dimensional] image generation
-
- 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
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
-
- 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
- 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
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jam et al. | A comprehensive review of past and present image inpainting methods | |
Saladi et al. | Analysis of denoising filters on MRI brain images | |
CN111784821B (en) | Three-dimensional model generation method and device, computer equipment and storage medium | |
CN110175986B (en) | Stereo image visual saliency detection method based on convolutional neural network | |
Abouelaziz et al. | No-reference mesh visual quality assessment via ensemble of convolutional neural networks and compact multi-linear pooling | |
Bhowmik et al. | Visual attention-based image watermarking | |
Herzog et al. | NoRM: No‐reference image quality metric for realistic image synthesis | |
Fyffe et al. | Comprehensive facial performance capture | |
Vitoria et al. | Semantic image inpainting through improved wasserstein generative adversarial networks | |
Muhammad et al. | Spec-Net and Spec-CGAN: Deep learning models for specularity removal from faces | |
Wang et al. | No-reference synthetic image quality assessment with convolutional neural network and local image saliency | |
CN112613460B (en) | Face generation model building method and face generation method | |
Sai Hareesh et al. | Exemplar-based color image inpainting: a fractional gradient function approach | |
Pang et al. | Progressive polarization based reflection removal via realistic training data generation | |
Polasek et al. | Vision UFormer: Long-range monocular absolute depth estimation | |
Hareesh et al. | A fast and simple gradient function guided filling order prioritization for exemplar-based color image inpainting | |
Tian et al. | Retinal fundus image superresolution generated by optical coherence tomography based on a realistic mixed attention GAN | |
Ngo et al. | Singe Image Dehazing With Unsharp Masking and Color Gamut Expansion | |
Li et al. | Super‐Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network | |
Kim et al. | Skin tactile surface restoration using deep learning from a mobile image: an application for virtual skincare | |
Kim et al. | Reliable perceptual loss computation for GAN-based super-resolution with edge texture metric | |
Zhang et al. | Image deblurring method based on self-attention and residual wavelet transform | |
Zhang et al. | Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video Detection | |
Liu et al. | Image denoising based on correlation adaptive sparse modeling | |
Pérez et al. | Enhancing Neural Rendering Methods with Image Augmentations |