Xu et al., 2020 - Google Patents

Learning to restore low-light images via decomposition-and-enhancement

Xu et al., 2020

View PDF
Document ID
15564945111335858156
Author
Xu K
Yang X
Yin B
Lau R
Publication year
Publication venue
Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

External Links

Snippet

Low-light images typically suffer from two problems. First, they have low visibility (ie, small pixel values). Second, noise becomes significant and disrupts the image content, due to low signal-to-noise ratio. Most existing lowlight image enhancement methods, however, learn …
Continue reading at openaccess.thecvf.com (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20172Image enhancement details
    • G06T2207/20182Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
    • 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
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001Image restoration
    • G06T5/003Deblurring; Sharpening
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20212Image combination
    • 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
    • G06T5/005Retouching; Inpainting; Scratch removal
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/20Special algorithmic details
    • G06T2207/20016Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • 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
    • G06T5/00Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/007Dynamic range modification
    • G06T5/008Local, e.g. shadow enhancement
    • 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
    • G06T5/50Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image by the use of more than one image, e.g. averaging, subtraction
    • 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
    • G06T5/20Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image by the use of local operators
    • 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
    • G06T11/002D [Two Dimensional] image generation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00Indexing scheme for image data processing or generation, in general
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04NPICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00Details of television systems
    • H04N5/222Studio circuitry; Studio devices; Studio equipment; Cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, TV cameras, video cameras, camcorders, webcams, camera modules for embedding in other devices, e.g. mobile phones, computers or vehicles
    • H04N5/225Television cameras; Cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, video cameras, camcorders, webcams, camera modules for embedding in other devices, e.g. mobile phones, computers or vehicles
    • H04N5/232Devices for controlling television cameras, e.g. remote control; Control of cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, TV cameras, video cameras, camcorders, webcams, camera modules for embedding in, e.g. mobile phones, computers or vehicles
    • H04N5/23229Devices for controlling television cameras, e.g. remote control; Control of cameras comprising an electronic image sensor, e.g. digital cameras, video cameras, TV cameras, video cameras, camcorders, webcams, camera modules for embedding in, e.g. mobile phones, computers or vehicles comprising further processing of the captured image without influencing the image pickup process

Similar Documents

Publication Publication Date Title
Xu et al. Learning to restore low-light images via decomposition-and-enhancement
Chen et al. Hdrunet: Single image hdr reconstruction with denoising and dequantization
Santos et al. Single image HDR reconstruction using a CNN with masked features and perceptual loss
Lim et al. DSLR: Deep stacked Laplacian restorer for low-light image enhancement
Yang et al. Sparse gradient regularized deep retinex network for robust low-light image enhancement
Wang et al. Lightening network for low-light image enhancement
Yue et al. Supervised raw video denoising with a benchmark dataset on dynamic scenes
Kalantari et al. Deep HDR video from sequences with alternating exposures
Liang et al. Cameranet: A two-stage framework for effective camera isp learning
TW202134997A (en) Method for denoising image, method for augmenting image dataset and user equipment
Afifi et al. Cie xyz net: Unprocessing images for low-level computer vision tasks
Xiong et al. Unsupervised low-light image enhancement with decoupled networks
Karadeniz et al. Burst photography for learning to enhance extremely dark images
Xu et al. Exploiting raw images for real-scene super-resolution
Huang et al. Hybrid image enhancement with progressive laplacian enhancing unit
WO2022100490A1 (en) Methods and systems for deblurring blurry images
Le et al. Single-image hdr reconstruction by multi-exposure generation
Rasheed et al. LSR: Lightening super-resolution deep network for low-light image enhancement
Panetta et al. Deep perceptual image enhancement network for exposure restoration
Zhao et al. Deep pyramid generative adversarial network with local and nonlocal similarity features for natural motion image deblurring
Wan et al. Purifying low-light images via near-infrared enlightened image
Saleem et al. A non-reference evaluation of underwater image enhancement methods using a new underwater image dataset
Tan et al. High dynamic range imaging for dynamic scenes with large-scale motions and severe saturation
Chen et al. Attention-based broad self-guided network for low-light image enhancement
Yadav et al. Frequency-domain loss function for deep exposure correction of dark images