[go: up one dir, main page]

Mei et al., 2020 - Google Patents

Spatial and spectral joint super-resolution using convolutional neural network

Mei et al., 2020

Document ID
3553255168634601397
Author
Mei S
Jiang R
Li X
Du Q
Publication year
Publication venue
IEEE Transactions on Geoscience and Remote Sensing

External Links

Snippet

Many applications have benefited from the images with both high spatial and spectral resolution, such as mineralogy and surveillance. However, it is difficult to acquire such images due to the limitation of sensor technologies. Recently, super-resolution (SR) …
Continue reading at ieeexplore.ieee.org (other versions)

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
    • 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
    • G06T3/4084—Transform-based scaling, e.g. FFT domain scaling
    • 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
    • G06T3/4061—Super resolution, i.e. output image resolution higher than sensor resolution by injecting details from a different spectral band
    • 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
    • G06T2207/10—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
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
    • 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
    • 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
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Similar Documents

Publication Publication Date Title
Mei et al. Spatial and spectral joint super-resolution using convolutional neural network
Liu et al. A spectral grouping and attention-driven residual dense network for hyperspectral image super-resolution
Li et al. Dual-stage approach toward hyperspectral image super-resolution
Li et al. Hyperspectral image super-resolution by band attention through adversarial learning
Wang et al. Hyperreconnet: Joint coded aperture optimization and image reconstruction for compressive hyperspectral imaging
Shao et al. Remote sensing image fusion with deep convolutional neural network
Zhou et al. Pyramid fully convolutional network for hyperspectral and multispectral image fusion
Zhang et al. LR-Net: Low-rank spatial-spectral network for hyperspectral image denoising
Dong et al. Laplacian pyramid dense network for hyperspectral pansharpening
Ma et al. Deep unfolding network for spatiospectral image super-resolution
Liu et al. Robust single image super-resolution via deep networks with sparse prior
Han et al. Hyperspectral and multispectral image fusion using optimized twin dictionaries
Li et al. RGB-induced feature modulation network for hyperspectral image super-resolution
Guo et al. Adaptive transform domain image super-resolution via orthogonally regularized deep networks
Tang et al. Deep inception-residual Laplacian pyramid networks for accurate single-image super-resolution
Xiao et al. Physics-based GAN with iterative refinement unit for hyperspectral and multispectral image fusion
Mishra et al. Self-FuseNet: Data free unsupervised remote sensing image super-resolution
Dong et al. Fusion of hyperspectral and panchromatic images using generative adversarial network and image segmentation
Khader et al. NMF-DuNet: Nonnegative matrix factorization inspired deep unrolling networks for hyperspectral and multispectral image fusion
Han et al. Deep residual network of spectral and spatial fusion for hyperspectral image super-resolution
Liu et al. An efficient residual learning neural network for hyperspectral image superresolution
CN117635427A (en) Hyperspectral image super-resolution method and system based on spectrum-space strategy combined network
Tan et al. Frequency-spatial domain feature fusion for spectral super-resolution
Li et al. Progressive spatial information-guided deep aggregation convolutional network for hyperspectral spectral super-resolution
Zhu et al. An adaptive multi-perceptual implicit sampling for hyperspectral and multispectral remote sensing image fusion