Nothing Special   »   [go: up one dir, main page]

Huang et al., 2020 - Google Patents

Multi-feature learning for low-light image enhancement

Huang et al., 2020

Document ID
1854769683773106564
Author
Huang W
Zhu Y
Wang R
Lu X
Publication year
Publication venue
Twelfth International Conference on Digital Image Processing (ICDIP 2020)

External Links

Snippet

Low-light images are not suitable for direct use in computer vision tasks due to the low visibility of the images. The existing low-light image enhancement methods usually produce colour distortion and noise amplification. This paper proposes a low-light image …
Continue reading at www.spiedigitallibrary.org (other versions)

Classifications

    • 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
    • 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
    • G06T2207/20172—Image enhancement 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • 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
    • G06T2207/30004—Biomedical image processing
    • 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
    • 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
    • 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
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • 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
    • G06T2200/00—Indexing scheme for image data processing or generation, in general

Similar Documents

Publication Publication Date Title
Lu et al. Multi-scale adversarial network for underwater image restoration
Salazar-Colores et al. Single image dehazing using a multilayer perceptron
Ding et al. Single image rain and snow removal via guided L0 smoothing filter
Sabir et al. Segmentation-based image defogging using modified dark channel prior
Van Nguyen et al. Single maritime image defogging based on illumination decomposition using texture and structure priors
Ruchay et al. Accuracy analysis of 3D object shape recovery using depth filtering algorithms
Lu et al. Pyramid frequency network with spatial attention residual refinement module for monocular depth estimation
Guo et al. Low-light image enhancement with joint illumination and noise data distribution transformation
Frantc et al. Machine learning approach for objective inpainting quality assessment
Yu et al. VIFNet: An end-to-end visible-infrared fusion network for image dehazing
Kumar et al. Dynamic stochastic resonance and image fusion based model for quality enhancement of dark and hazy images
Huang et al. Multi-feature learning for low-light image enhancement
Yao et al. A multi-expose fusion image dehazing based on scene depth information
Yu et al. Attention based dual path fusion networks for multi-focus image
Oludare et al. Attention-guided cascaded networks for improved face detection and landmark localization under low-light conditions
Yang et al. Detail-preserving single nighttime image dehazing
Gao et al. Air infrared small target local dehazing based on multiple-factor fusion cascade network
Wang et al. Single image deraining using deep convolutional networks
Suganya et al. Hybrid gated recurrent unit and convolutional neural network-based deep learning architecture-based visibility improvement scheme for improving fog-degraded images
Chang et al. Robust ghost-free multiexposure fusion for dynamic scenes
Li et al. Low-light image enhancement based on variational retinex model
Hu et al. Dehazing for images with sun in the sky
Goyal et al. Detailed-based dictionary learning for low-light image enhancement using camera response model for industrial applications
Yang et al. Polarization image defogging based on detail recovery generative adversarial network
Qin et al. A summary of research progress of single image to remove rain and fog based on deep learning