Rai et al., 2023 - Google Patents
Low-light robust face image super-resolution via neuro-fuzzy inferencing-based locality constrained representationRai et al., 2023
- Document ID
- 11836056163809049599
- Author
- Rai D
- Rajput S
- Publication year
- Publication venue
- IEEE Transactions on Instrumentation and Measurement
External Links
Snippet
Face super-resolution (FSR) has recently become an interesting topic of study in the fields of image processing and computer vision. However, current FSR methods are still incapable of dealing with those faces suffering from low-light issues, which are frequently caused by …
Classifications
-
- 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
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
- G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component 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
- G06K9/36—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
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
- G06K9/00268—Feature extraction; Face representation
- G06K9/00281—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
-
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/40—Analysis of texture
-
- 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
- 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
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tewari et al. | Diffusion with forward models: Solving stochastic inverse problems without direct supervision | |
| Pei et al. | Effects of image degradation and degradation removal to CNN-based image classification | |
| Jiang et al. | Decomposition makes better rain removal: An improved attention-guided deraining network | |
| Wang et al. | Cycle-snspgan: Towards real-world image dehazing via cycle spectral normalized soft likelihood estimation patch gan | |
| Lu et al. | Face hallucination via split-attention in split-attention network | |
| Zhang et al. | Multi-scale single image dehazing using perceptual pyramid deep network | |
| Jiang et al. | Noise robust face image super-resolution through smooth sparse representation | |
| Hu et al. | Incremental tensor subspace learning and its applications to foreground segmentation and tracking | |
| Zhang et al. | Robust visual tracking via basis matching | |
| Wang et al. | A survey of deep face restoration: Denoise, super-resolution, deblur, artifact removal | |
| Tuzel et al. | Global-local face upsampling network | |
| Rai et al. | Low-light robust face image super-resolution via neuro-fuzzy inferencing-based locality constrained representation | |
| Chen et al. | Robust face image super-resolution via joint learning of subdivided contextual model | |
| Shit et al. | An encoder‐decoder based CNN architecture using end to end dehaze and detection network for proper image visualization and detection | |
| CN109003291A (en) | Method for tracking target and device | |
| CN112419185A (en) | Precise high-reflection removal method based on light field iteration | |
| Parde et al. | Deep convolutional neural network features and the original image | |
| Tangsakul et al. | Single image haze removal using deep cellular automata learning | |
| Ye et al. | A unified model for continuous conditional video prediction | |
| Li et al. | Survey on deep face restoration: From non-blind to blind and beyond | |
| Chen et al. | Robust face super-resolution via position relation model based on global face context | |
| Ullah et al. | 2-D canonical correlation analysis based image super-resolution scheme for facial emotion recognition | |
| Ho et al. | Toward realistic single-view 3d object reconstruction with unsupervised learning from multiple images | |
| Kratzwald et al. | Improving video generation for multi-functional applications | |
| Liu et al. | Attentive semantic and perceptual faces completion using self-attention generative adversarial networks |