Aghajarian et al., 2020 - Google Patents
Deep learning algorithm for Gaussian noise removal from imagesAghajarian et al., 2020
View PDF- Document ID
- 9867502909981641458
- Author
- Aghajarian M
- McInroy J
- Muknahallipatna S
- Publication year
- Publication venue
- Journal of Electronic Imaging
External Links
Snippet
A deep learning algorithm for Gaussian noise removal from both grayscale and color images is developed. As opposed to most existing discriminative methods that train a specific model for each noise level, the proposed method can handle a wide range of noise levels using …
- 238000000034 method 0 abstract description 14
Classifications
-
- 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
- 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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
-
- 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
- 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
-
- 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
- G06K9/20—Image acquisition
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
-
- 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
-
- 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
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Salazar-Colores et al. | Single image dehazing using a multilayer perceptron | |
Zhan et al. | Fast filtering image fusion | |
Wu et al. | Single-image super-resolution based on Markov random field and contourlet transform | |
Zhan et al. | Multifocus image fusion using phase congruency | |
Yang et al. | Multi-focus image fusion via clustering PCA based joint dictionary learning | |
Aghajarian et al. | Deep learning algorithm for Gaussian noise removal from images | |
Tian et al. | SWDGAN: GAN-based sampling and whole image denoising network for compressed sensing image reconstruction | |
Li et al. | Multifocus image fusion method for image acquisition of 3D objects | |
Zhu et al. | Super-resolved image perceptual quality improvement via multifeature discriminators | |
Wu et al. | Hybrid regularization model combining overlapping group sparse second-order total variation and nonconvex total variation | |
Li et al. | Dilated residual encode–decode networks for image denoising | |
Wang et al. | Multi-focus image fusion based on gradient tensor HOSVD | |
Zhang et al. | Coarse-to-fine multiscale fusion network for single image deraining | |
Zhang et al. | Unsupervised clustering for logo images using singular values region covariance matrices on Lie groups | |
Kumar et al. | Encoder–decoder-based CNN model for detection of object removal by image inpainting | |
Fan et al. | Unified framework based on multiscale transform and feature learning for infrared and visible image fusion | |
Al-Jaberi et al. | Topological data analysis to improve exemplar-based inpainting | |
Panda et al. | Exponential linear unit dilated residual network for digital image denoising | |
Sang et al. | MoNET: no-reference image quality assessment based on a multi-depth output network | |
Selvanambi et al. | Image denoising using block matching and convolutional neural network | |
Wang et al. | Single image rain removal via densely connected contextual and semantic correlation net | |
Cao et al. | A successive approach to enhancement of infrared facial images | |
Abdelhamid et al. | Adaptive gamma correction-based expert system for nonuniform illumination face enhancement | |
Miao et al. | Structure descriptor based on just noticeable difference for texture image classification | |
Wang et al. | Multiscale deep network for compressive sensing image reconstruction |