[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Liu et al., 2020 - Google Patents

Learning cascaded convolutional networks for blind single image super-resolution

Liu et al., 2020

Document ID
5071602664197466662
Author
Liu P
Zhang H
Cao Y
Liu S
Ren D
Zuo W
Publication year
Publication venue
Neurocomputing

External Links

Snippet

This paper studies the blind super-resolution of real low-quality and low-resolution (LR) images. Existing convolutional network (CNN) based approaches usually learn a single image super-resolution (SISR) model for a specific downsampler (eg, bicubic …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/40Scaling the whole image or part thereof
    • G06T3/4053Super resolution, i.e. output image resolution higher than sensor resolution
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/40Scaling the whole image or part thereof
    • G06T3/4084Transform-based scaling, e.g. FFT domain scaling
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20048Transform domain processing
    • G06T2207/20064Wavelet transform [DWT]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T3/00Geometric image transformation in the plane of the image, e.g. from bit-mapped to bit-mapped creating a different image
    • G06T3/40Scaling the whole image or part thereof
    • G06T3/4007Interpolation-based scaling, e.g. bilinear interpolation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62Methods or arrangements for recognition using electronic means
    • G06K9/6217Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247Extracting 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
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/10Image acquisition modality
    • G06T2207/10024Color image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001Image restoration
    • G06T5/002Denoising; Smoothing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • G06T11/003Reconstruction from projections, e.g. tomography
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46Extraction of features or characteristics of the image
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00Indexing scheme for image data processing or generation, in general
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2211/00Image generation
    • G06T2211/40Computed tomography
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10Complex mathematical operations

Similar Documents

Publication Publication Date Title
Zhang et al. Multiple cycle-in-cycle generative adversarial networks for unsupervised image super-resolution
Su et al. A survey of deep learning approaches to image restoration
US20210264568A1 (en) Super resolution using a generative adversarial network
Lan et al. MADNet: A fast and lightweight network for single-image super resolution
Zhang et al. Deep unfolding network for image super-resolution
Zhang et al. Image super-resolution based on structure-modulated sparse representation
Ledig et al. Photo-realistic single image super-resolution using a generative adversarial network
Zeng et al. Coupled deep autoencoder for single image super-resolution
Zhang et al. Learning multiple linear mappings for efficient single image super-resolution
Liu et al. Learning cascaded convolutional networks for blind single image super-resolution
Ren et al. Single image super-resolution using local geometric duality and non-local similarity
Li et al. FilterNet: Adaptive information filtering network for accurate and fast image super-resolution
Fu et al. Residual scale attention network for arbitrary scale image super-resolution
Tang et al. Deep inception-residual Laplacian pyramid networks for accurate single-image super-resolution
Vu et al. Perception-enhanced image super-resolution via relativistic generative adversarial networks
Zhang et al. Collaborative representation cascade for single-image super-resolution
CN106127689A (en) Image/video super-resolution method and device
López-Tapia et al. A single video super-resolution GAN for multiple downsampling operators based on pseudo-inverse image formation models
Umer et al. Deep cyclic generative adversarial residual convolutional networks for real image super-resolution
Huang et al. Learning deformable and attentive network for image restoration
Mikaeli et al. Single-image super-resolution via patch-based and group-based local smoothness modeling
Sharma et al. An efficient image super resolution model with dense skip connections between complex filter structures in Generative Adversarial Networks
Kim et al. Example-based learning for single-image super-resolution and JPEG artifact removal
Wang et al. Image super-resolution using only low-resolution images
Vella et al. Single image super-resolution via CNN architectures and TV-TV minimization