[go: up one dir, main page]

Xiaowei et al., 2019 - Google Patents

Fs-net: Medical image denoising via local receptive field smoothing network

Xiaowei et al., 2019

Document ID
9388753560600167946
Author
Xiaowei W
Xiong J
Wang R
Sori W
Wang J
Shaohui L
Jiang F
Publication year
Publication venue
2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC)

External Links

Snippet

Noise is one of the most challenging tasks in medical images which is happening during transmission and acquisition, and as a result, it complicates a subsequent medical image analysis task. This paper proposes a novel local receptive field smoothing network (FS-Net) …
Continue reading at ieeexplore.ieee.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
    • G06T2207/20—Special algorithmic details
    • G06T2207/20048—Transform domain processing
    • G06T2207/20064—Wavelet transform [DWT]
    • 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
    • G06T2207/20048—Transform domain processing
    • G06T2207/20056—Discrete and fast Fourier transform, [DFT, FFT]
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • 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
    • G06T2207/20182—Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering
    • 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
    • G06T2207/10072—Tomographic images
    • G06T2207/10104—Positron emission tomography [PET]
    • 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
    • G06T5/001—Image restoration
    • G06T5/002—Denoising; Smoothing
    • 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
    • G06T2207/20016—Hierarchical, coarse-to-fine, multiscale or multiresolution image processing; Pyramid transform
    • 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
    • G06T7/0012—Biomedical image inspection
    • 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
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/20—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image by the use of local operators
    • 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
    • G06T3/4053—Super resolution, i.e. output image resolution higher than sensor resolution
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/00—2D [Two Dimensional] image generation

Similar Documents

Publication Publication Date Title
Kaur et al. A complete review on image denoising techniques for medical images
CN108898642B (en) A sparse angle CT imaging method based on convolutional neural network
Fang et al. Blind visual quality assessment for image super-resolution by convolutional neural network
Ye et al. Deep back projection for sparse-view CT reconstruction
Li et al. FilterNet: Adaptive information filtering network for accurate and fast image super-resolution
Hu et al. SPECIAL: Single-shot projection error correction integrated adversarial learning for limited-angle CT
Min et al. Blind deblurring via a novel recursive deep CNN improved by wavelet transform
Hou et al. CT image quality enhancement via a dual-channel neural network with jointing denoising and super-resolution
Upadhyay et al. Uncertainty-aware gan with adaptive loss for robust mri image enhancement
Li et al. Perceptual quality evaluation for image defocus deblurring
Li et al. Multi-scale feature fusion network for low-dose CT denoising
Lepcha et al. A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing
Schirrmacher et al. Temporal and volumetric denoising via quantile sparse image prior
Liu et al. Progressive residual learning with memory upgrade for ultrasound image blind super-resolution
Singh et al. Microscopic biopsy image reconstruction using inception block with denoising auto-encoder approach
Lepcha et al. Retracted: An efficient medical image super resolution based on piecewise linear regression strategy using domain transform filtering
Jin et al. Low-dose CT image restoration based on noise prior regression network
Ferzo et al. Image denoising techniques using unsupervised machine learning and deep learning algorithms: A review
Liu et al. SDCNet: Smoothed dense-convolution network for restoring low-dose cerebral CT perfusion
Tian et al. Retinal fundus image superresolution generated by optical coherence tomography based on a realistic mixed attention GAN
Zin et al. Local image denoising using RAISR
Guo Real-time medical image denoising and information hiding model based on deep wavelet multiscale autonomous unmanned analysis: G. Guo
Xiaowei et al. Fs-net: Medical image denoising via local receptive field smoothing network
Singh et al. HoEnTOA: Holoentropy and Taylor assisted optimization based novel image quality enhancement algorithm for multi-focus image fusion
Yang et al. MIN-MEF: Multiscale Interaction Network for Multiexposure Image Fusion