Song et al., 2020 - Google Patents
Efficient residual dense block search for image super-resolutionSong et al., 2020
View PDF- Document ID
- 2696346684302966555
- Author
- Song D
- Xu C
- Jia X
- Chen Y
- Xu C
- Wang Y
- Publication year
- Publication venue
- Proceedings of the AAAI conference on artificial intelligence
External Links
Snippet
Although remarkable progress has been made on single image super-resolution due to the revival of deep convolutional neural networks, deep learning methods are confronted with the challenges of computation and memory consumption in practice, especially for mobile …
- 238000011176 pooling 0 abstract description 22
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
- G06K9/36—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
- G06K9/62—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
-
- 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
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- 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
-
- 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
- G06F17/10—Complex mathematical operations
-
- 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
- G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Song et al. | Efficient residual dense block search for image super-resolution | |
CN106991646B (en) | Image super-resolution method based on dense connection network | |
Xie et al. | Learning frequency-aware dynamic network for efficient super-resolution | |
Yang et al. | A constant-space belief propagation algorithm for stereo matching | |
Ma et al. | Image superresolution via dense discriminative network | |
Gendy et al. | Lightweight image super-resolution based on deep learning: State-of-the-art and future directions | |
Xu et al. | Boosting the performance of plug-and-play priors via denoiser scaling | |
CN113744136A (en) | Image super-resolution reconstruction method and system based on channel constraint multi-feature fusion | |
Kim et al. | Restoring spatially-heterogeneous distortions using mixture of experts network | |
CN116090517A (en) | Model training method, object detection device, and readable storage medium | |
CN114897690A (en) | Lightweight image super-resolution method based on serial high-frequency attention | |
Cherel et al. | A patch-based algorithm for diverse and high fidelity single image generation | |
Liu et al. | A fast and accurate super-resolution network using progressive residual learning | |
Gong et al. | A superpixel segmentation algorithm based on differential evolution | |
Bai et al. | Digital rock core images resolution enhancement with improved super resolution convolutional neural networks | |
Xiang et al. | Optical flow estimation using spatial-channel combinational attention-based pyramid networks | |
Wang et al. | Face super-resolution via hierarchical multi-scale residual fusion network | |
CN117576497A (en) | Training method and device for memory Dirichlet process Gaussian mixture model | |
Zhao et al. | Multi-Objective Net Architecture Pruning for Remote Sensing Classification | |
CN116384471A (en) | Model pruning method, device, computer equipment, storage medium and program product | |
Wąsala et al. | AutoSR4EO: An AutoML Approach to Super-Resolution for Earth Observation Images | |
Huang et al. | TARN: a lightweight two-branch adaptive residual network for image super-resolution | |
Cheng et al. | Single image super-resolution via laplacian information distillation network | |
Lu et al. | Face hallucination using manifold-regularized group locality-constrained representation | |
Xing et al. | Image super-resolution using aggregated residual transformation networks with spatial attention |