[go: up one dir, main page]

Cho et al., 2022 - Google Patents

Kernel estimation for super-resolution with flow-based kernel prior

Cho et al., 2022

Document ID
6067503872174925973
Author
Cho S
Cho N
Publication year
Publication venue
International Workshop on Advanced Imaging Technology (IWAIT) 2022

External Links

Snippet

Single-Image Super-Resolution methods typically assume that a low-resolution image is degraded from a high-resolution one through “bicubic” kernel convolution followed by downscaling. However, this induces a domain gap between training image datasets and the …
Continue reading at www.spiedigitallibrary.org (other versions)

Classifications

    • 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
    • G06F17/30244—Information retrieval; Database structures therefor; File system structures therefor in image databases
    • G06F17/30247—Information retrieval; Database structures therefor; File system structures therefor in image databases based on features automatically derived from the image data
    • 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
    • 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
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • 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
    • 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
    • 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
    • G06T3/0068—Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image for image registration, e.g. elastic snapping
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • 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
Pickup et al. Bayesian methods for image super-resolution
US9692939B2 (en) Device, system, and method of blind deblurring and blind super-resolution utilizing internal patch recurrence
Wu et al. Single-image super-resolution based on Markov random field and contourlet transform
Huang et al. Face hallucination using convolutional neural network with iterative back projection
Kawulok et al. Deep learning for fast super-resolution reconstruction from multiple images
Lu et al. Dense U-Net for single image super-resolution using shuffle pooling
US20140169700A1 (en) Bayesian approach to alignment-based image hallucination
Zhang et al. Hybrid sparse-representation-based approach to image super-resolution reconstruction
Cho et al. Kernel estimation for super-resolution with flow-based kernel prior
Faragallah Enhancing multispectral imagery spatial resolution using optimized adaptive PCA and high-pass modulation
Li et al. Dilated residual encode–decode networks for image denoising
Xu et al. Single‐Image Super‐Resolution Using Panchromatic Gradient Prior and Variational Model
Hu et al. Single MR image super-resolution via mixed self-similarity attention network
Tang et al. Edge and color preserving single image superresolution
Trahearn et al. A fast method for approximate registration of whole-slide images of serial sections using local curvature
Gass et al. Detection and correction of inconsistency-based errors in non-rigid registration
Ren et al. Registration of multitemporal low-resolution synthetic aperture radar images based on a new similarity measure
Wang et al. Fast image super-resolution with the simplified residual network
Yin et al. Super-resolution reconstruction of remote sensing images based on multi-scale residual networks
Li et al. Nonlocal variational model for saliency detection
Shi et al. A method for the super-resolution reconstruction of remote sensing images based on multi-scale feature fusion and an attention mechanism
Tang et al. Partial least-squares regression on common feature space for single image superresolution
Wang et al. Unsupervised contrastive learning-based image super-resolution algorithm
Duangprom et al. Deep attentive pixels for face super-resolution
Wang et al. RSSR-CDM: remote sensing image super-resolution based on an improved conditional diffusion model