Yin et al., 2023 - Google Patents
Unpaired low-dose CT denoising via an improved cycle-consistent adversarial network with attention ensembleYin et al., 2023
- Document ID
- 12745474863869053585
- Author
- Yin Z
- Xia K
- Wang S
- He Z
- Zhang J
- Zu B
- Publication year
- Publication venue
- The Visual Computer
External Links
Snippet
Many deep learning-based approaches have been authenticated well performed for low- dose computed tomography (LDCT) image postprocessing. Unfortunately, most of them highly depend on well-paired datasets, which are difficult to acquire in clinical practice …
- 238000000034 method 0 abstract description 126
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
- G06T2207/10072—Tomographic images
- G06T2207/10104—Positron emission tomography [PET]
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color 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
- G06T5/001—Image restoration
- G06T5/002—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/003—Reconstruction from projections, e.g. tomography
- G06T11/006—Inverse problem, transformation from projection-space into object-space, e.g. transform methods, back-projection, algebraic methods
-
- 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
- G06T2207/20076—Probabilistic 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
- G06T2207/20112—Image segmentation 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
- G06T5/007—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
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2211/00—Image generation
- G06T2211/40—Computed tomography
-
- 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
- G06T2200/00—Indexing scheme for image data processing or generation, in general
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gholizadeh-Ansari et al. | Deep learning for low-dose CT denoising using perceptual loss and edge detection layer | |
Xie et al. | Artifact removal using improved GoogLeNet for sparse-view CT reconstruction | |
Niu et al. | Noise suppression with similarity-based self-supervised deep learning | |
Tang et al. | Unpaired low‐dose CT denoising network based on cycle‐consistent generative adversarial network with prior image information | |
Liu et al. | Deep iterative reconstruction estimation (DIRE): approximate iterative reconstruction estimation for low dose CT imaging | |
Li et al. | Low‐dose CT image denoising with improving WGAN and hybrid loss function | |
Yang et al. | High-frequency sensitive generative adversarial network for low-dose CT image denoising | |
Yin et al. | Unpaired low-dose CT denoising via an improved cycle-consistent adversarial network with attention ensemble | |
Du et al. | Stacked competitive networks for noise reduction in low-dose CT | |
Li et al. | Incorporation of residual attention modules into two neural networks for low‐dose CT denoising | |
Zhu et al. | STEDNet: Swin transformer‐based encoder–decoder network for noise reduction in low‐dose CT | |
Feng et al. | Dual residual convolutional neural network (DRCNN) for low-dose CT imaging | |
Zhao et al. | Dual-scale similarity-guided cycle generative adversarial network for unsupervised low-dose CT denoising | |
Liu et al. | MRCON-Net: Multiscale reweighted convolutional coding neural network for low-dose CT imaging | |
Huang et al. | Segmentation-guided denoising network for low-dose CT imaging | |
Li et al. | Learning non-local perfusion textures for high-quality computed tomography perfusion imaging | |
Li et al. | A comprehensive survey on deep learning techniques in CT image quality improvement | |
Liu et al. | Learning low‐dose CT degradation from unpaired data with flow‐based model | |
Xia et al. | Deep residual neural network based image enhancement algorithm for low dose CT images | |
Chan et al. | An attention-based deep convolutional neural network for ultra-sparse-view CT reconstruction | |
Li et al. | Multi-scale feature fusion network for low-dose CT denoising | |
Chen et al. | Deep learning-based algorithms for low-dose CT imaging: A review | |
Huang et al. | MLNAN: Multi-level noise-aware network for low-dose CT imaging implemented with constrained cycle Wasserstein generative adversarial networks | |
Izadi et al. | Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks | |
Zhou et al. | GMRE-iUnet: Isomorphic Unet fusion model for PET and CT lung tumor images |