Xie et al., 2018 - Google Patents
Deep convolutional networks with residual learning for accurate spectral-spatial denoisingXie et al., 2018
- Document ID
- 4732803615816247294
- Author
- Xie W
- Li Y
- Jia X
- Publication year
- Publication venue
- Neurocomputing
External Links
Snippet
Although hyperspectral image (HSI) denoising has been studied for decades, preserving spectral data efficiently remains an open problem. In this paper, we present a powerful and trainable spectral difference mapping method based on convolutional networks with residual …
- 230000003595 spectral 0 abstract description 73
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
- 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
- G06K9/6201—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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
-
- 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
- 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
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
-
- 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
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- 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
- G06T5/007—Dynamic range modification
- G06T5/008—Local, e.g. shadow enhancement
-
- 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
- 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
- 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
- G06K9/20—Image acquisition
-
- 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
- G06T11/00—2D [Two Dimensional] image generation
-
- 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
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Xie et al. | Deep convolutional networks with residual learning for accurate spectral-spatial denoising | |
Xie et al. | Hyperspectral image super-resolution using deep feature matrix factorization | |
Zhou et al. | Semantic-supervised infrared and visible image fusion via a dual-discriminator generative adversarial network | |
Zhang et al. | LR-Net: Low-rank spatial-spectral network for hyperspectral image denoising | |
Nguyen et al. | Hyperspectral image denoising using SURE-based unsupervised convolutional neural networks | |
Li et al. | Low-light image enhancement with knowledge distillation | |
Xie et al. | High-quality spectral-spatial reconstruction using saliency detection and deep feature enhancement | |
Pan et al. | SQAD: Spatial-spectral quasi-attention recurrent network for hyperspectral image denoising | |
Li et al. | RGB-induced feature modulation network for hyperspectral image super-resolution | |
Li et al. | Infrared-visible image fusion method based on sparse and prior joint saliency detection and LatLRR-FPDE | |
Wang et al. | Multi-focus image fusion based on quad-tree decomposition and edge-weighted focus measure | |
Xie et al. | Trainable spectral difference learning with spatial starting for hyperspectral image denoising | |
Liu et al. | Infrared and visible image fusion and denoising via ℓ2− ℓp norm minimization | |
Rahman et al. | Diverse image enhancer for complex underexposed image | |
Wong et al. | Hsi-ipnet: Hyperspectral imagery inpainting by deep learning with adaptive spectral extraction | |
Yang et al. | Low‐light image enhancement based on Retinex decomposition and adaptive gamma correction | |
Yin et al. | Adaptive low light visual enhancement and high-significant target detection for infrared and visible image fusion | |
Yang et al. | MSE-Net: generative image inpainting with multi-scale encoder | |
Wali et al. | Recent progress in digital image restoration techniques: a review | |
Yu et al. | Two-stage image decomposition and color regulator for low-light image enhancement | |
Su et al. | Physical model and image translation fused network for single-image dehazing | |
Huang et al. | A prior-guided deep network for real image denoising and its applications | |
Meng et al. | Gia-net: Global information aware network for low-light imaging | |
Wang et al. | Tuning-free plug-and-play hyperspectral image deconvolution with deep priors | |
Luo et al. | Infrared and visible image fusion based on VPDE model and VGG network |