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

Nayak et al., 2023 - Google Patents

DMF-Net: a deep multi-level semantic fusion network for high-resolution chest CT and X-ray image de-noising

Nayak et al., 2023

View HTML @Full View
Document ID
15228673911314626299
Author
Nayak T
Annavarappu C
Nayak S
Gedefaw B
Publication year
Publication venue
BMC Medical Imaging

External Links

Snippet

Medical images such as CT and X-ray have been widely used for the detection of several chest infections and lung diseases. However, these images are susceptible to different types of noise, and it is hard to remove these noises due to their complex distribution. The …
Continue reading at link.springer.com (HTML) (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
    • G06T2207/30—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
    • G06T2207/20048—Transform domain processing
    • G06T2207/20064—Wavelet transform [DWT]
    • 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
    • G06T5/00—Image enhancement or restoration, e.g. from bit-mapped to bit-mapped creating a similar image
    • G06T5/001—Image restoration
    • G06T5/003—Deblurring; Sharpening
    • 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
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10116—X-ray 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
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • 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
    • 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
    • G06T2207/20172—Image enhancement details
    • 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
    • G06K9/52—Extraction of features or characteristics of the image by deriving mathematical or geometrical properties from the whole image
    • G06K9/527—Scale-space domain transformation, e.g. with wavelet analysis
    • 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
    • G06T2211/00—Image generation
    • 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

Similar Documents

Publication Publication Date Title
Xie et al. Artifact removal using improved GoogLeNet for sparse-view CT reconstruction
Kang et al. A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction
US10475214B2 (en) Tomographic reconstruction based on deep learning
Chanu et al. Retracted article: computer-aided detection of brain tumor from magnetic resonance images using deep learning network
Kaur et al. A complete review on image denoising techniques for medical images
Zhang et al. Noise2Context: context‐assisted learning 3D thin‐layer for low‐dose CT
Goyal et al. Multimodal image fusion and denoising in NSCT domain using CNN and FOTGV
Amaranageswarao et al. Wavelet based medical image super resolution using cross connected residual-in-dense grouped convolutional neural network
Sahu et al. An application of deep dual convolutional neural network for enhanced medical image denoising
Zhu et al. STEDNet: Swin transformer‐based encoder–decoder network for noise reduction in low‐dose CT
Yin et al. Unpaired low-dose CT denoising via an improved cycle-consistent adversarial network with attention ensemble
Wang et al. Dual-domain adaptive-scaling non-local network for CT metal artifact reduction
Dinh A novel approach using the local energy function and its variations for medical image fusion
Abuya et al. An Image Denoising Technique Using Wavelet-Anisotropic Gaussian Filter-Based Denoising Convolutional Neural Network for CT Images
Nayak et al. DMF-Net: a deep multi-level semantic fusion network for high-resolution chest CT and X-ray image de-noising
Patwari et al. Measuring CT reconstruction quality with deep convolutional neural networks
Chillaron et al. Evaluation of image filters for their integration with LSQR computerized tomography reconstruction method
Lepcha et al. A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing
Liang Medical image fusion with deep neural networks
Dinh et al. Medical image fusion based on transfer learning techniques and coupled neural P systems
Karimi et al. Reducing streak artifacts in computed tomography via sparse representation in coupled dictionaries
Yang et al. X‐Ray Breast Images Denoising Method Based on the Convolutional Autoencoder
Siddiqi et al. [Retracted] Signal‐to‐Noise Ratio Comparison of Several Filters against Phantom Image
Niknejad Mazandarani et al. Low-Dose CT Image Denoising with a Residual Multi-scale Feature Fusion Convolutional Neural Network and Enhanced Perceptual Loss
Won et al. Low-dose CT denoising using octave convolution with high and low frequency bands