Titarenko et al., 2022 - Google Patents
Study of the ability of neural networks to extract and use semantic information when they are trained to reconstruct noisy imagesTitarenko et al., 2022
- Document ID
- 5837660596208623956
- Author
- Titarenko M
- Malashin R
- Publication year
- Publication venue
- Journal of Optical Technology
External Links
Snippet
Subject of study. This paper discusses how deep convolutional neural networks can be used to improve images obtained under noisy conditions when supplementary information concerning objects on the image is input in the form of segmentation masks. Several …
- 230000001537 neural 0 title abstract description 11
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
- G06T2207/10—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
- 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
- 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
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/001—Texturing; Colouring; Generation of texture or colour
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T11/00—2D [Two Dimensional] image generation
- G06T11/40—Filling a planar surface by adding surface attributes, e.g. colour or texture
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/50—Lighting effects
-
- 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
- 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
- G06T1/00—General purpose image data processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Liu et al. | TomoGAN: low-dose synchrotron x-ray tomography with generative adversarial networks: discussion | |
US11887311B2 (en) | Method and apparatus for segmenting a medical image, and storage medium | |
Anwar et al. | Diving deeper into underwater image enhancement: A survey | |
Kang et al. | Stainnet: a fast and robust stain normalization network | |
US8264500B2 (en) | Image masks generated from local color models | |
Hong et al. | DNN-VolVis: Interactive volume visualization supported by deep neural network | |
Lu et al. | Deep texture and structure aware filtering network for image smoothing | |
Zhou et al. | High-frequency details enhancing DenseNet for super-resolution | |
Guo et al. | Haze removal for single image: A comprehensive review | |
Li et al. | Speckle noise removal based on structural convolutional neural networks with feature fusion for medical image | |
Titarenko et al. | Study of the ability of neural networks to extract and use semantic information when they are trained to reconstruct noisy images | |
CN114972382A (en) | Brain tumor segmentation algorithm based on lightweight UNet + + network | |
Mathur et al. | 2D to 3D medical image colorization | |
Cao et al. | A novel image multitasking enhancement model for underwater crack detection | |
Tabkha et al. | Semantic enrichment of point cloud by automatic extraction and enhancement of 360° panoramas | |
Babu et al. | MRI and CT image fusion using cartoon-texture and QWT decomposition and cuckoo search-grey wolf optimization | |
Levinski et al. | Interactive surface-guided segmentation of brain MRI data | |
Song et al. | Vector regression functions for texture compression | |
Wu et al. | Non‐uniform image blind deblurring by two‐stage fully convolution network | |
Lan et al. | Unpaired stain style transfer using invertible neural networks based on channel attention and long-range residual | |
Prasad et al. | Digital Image Enhancement using Conventional Neural Network | |
Syamala et al. | Brain MRI image bias correction using generative adversarial network | |
Nystrom et al. | Segmentation and visualization of 3D medical images through haptic rendering | |
KR102648938B1 (en) | Method and apparatus for 3D image reconstruction based on few-shot neural radiance fields using geometric consistency | |
CN117422927B (en) | Mammary gland ultrasonic image classification method, system, electronic equipment and medium |